TextAnalyticsClient Class
- java.
lang. Object - com.
azure. ai. textanalytics. TextAnalyticsClient
- com.
public final class TextAnalyticsClient
This class provides a synchronous client that contains all the operations that apply to Azure Text Analytics. Operations allowed by the client are language detection, entities recognition, linked entities recognition, key phrases extraction, and sentiment analysis of a document or a list of documents.
Getting Started
In order to interact with the Text Analytics features in Azure AI Language Service, you'll need to create an instance of the TextAnalyticsClient. To make this possible you'll need the key credential of the service. Alternatively, you can use AAD authentication via Azure Identity to connect to the service.
- Azure Key Credential, see credential(AzureKeyCredential keyCredential).
- Azure Active Directory, see credential(TokenCredential tokenCredential).
Sample: Construct Synchronous Text Analytics Client with Azure Key Credential
The following code sample demonstrates the creation of a TextAnalyticsClient, using the TextAnalyticsClientBuilder to configure it with a key credential.
TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClientBuilder()
.credential(new AzureKeyCredential("{key}"))
.endpoint("{endpoint}")
.buildClient();
View TextAnalyticsClientBuilder for additional ways to construct the client.
See methods in client level class below to explore all features that library provides.
Extract information
Text Analytics client can use Natural Language Understanding (NLU) to extract information from unstructured text. For example, identify key phrases or Personally Identifiable, etc. Below you can look at the samples on how to use it.
Key Phrases Extraction
The extractKeyPhrases(String document) method can be used to extract key phrases, which returns a list of strings denoting the key phrases in the document.
KeyPhrasesCollection extractedKeyPhrases =
textAnalyticsClient.extractKeyPhrases("My cat might need to see a veterinarian.");
for (String keyPhrase : extractedKeyPhrases) {
System.out.printf("%s.%n", keyPhrase);
}
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Named Entities Recognition(NER): Prebuilt Model
The recognizeEntities(String document) method can be used to recognize entities, which returns a list of general categorized entities in the provided document.
CategorizedEntityCollection recognizeEntitiesResult =
textAnalyticsClient.recognizeEntities("Satya Nadella is the CEO of Microsoft");
for (CategorizedEntity entity : recognizeEntitiesResult) {
System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore());
}
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Custom Named Entities Recognition(NER): Custom Model
The beginRecognizeCustomEntities(Iterable<String> documents, String projectName, String deploymentName) method can be used to recognize custom entities, which returns a list of custom entities for the provided list of document.
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
+ "in oil and natural gas development on federal lands over the past six years has stretched the"
+ " staff of the BLM to a point that it has been unable to meet its environmental protection "
+ "responsibilities."); }
SyncPoller<RecognizeCustomEntitiesOperationDetail, RecognizeCustomEntitiesPagedIterable> syncPoller =
textAnalyticsClient.beginRecognizeCustomEntities(documents, "{project_name}", "{deployment_name}");
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (RecognizeEntitiesResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (CategorizedEntity entity : documentResult.getEntities()) {
System.out.printf(
"\tText: %s, category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore());
}
}
});
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Linked Entities Recognition
The recognizeLinkedEntities(String document) method can be used to find linked entities, which returns a list of recognized entities with links to a well-known knowledge base for the provided document.
String document = "Old Faithful is a geyser at Yellowstone Park.";
System.out.println("Linked Entities:");
textAnalyticsClient.recognizeLinkedEntities(document).forEach(linkedEntity -> {
System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
linkedEntity.getDataSource());
linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
"Matched entity: %s, confidence score: %f.%n",
entityMatch.getText(), entityMatch.getConfidenceScore()));
});
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Personally Identifiable Information(PII) Entities Recognition
The recognizePiiEntities(String document) method can be used to recognize PII entities, which returns a list of Personally Identifiable Information(PII) entities in the provided document.
For a list of supported entity types, check: this.
PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities("My SSN is 859-98-0987");
System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
for (PiiEntity entity : piiEntityCollection) {
System.out.printf(
"Recognized Personally Identifiable Information entity: %s, entity category: %s,"
+ " entity subcategory: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore());
}
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Text Analytics for Health: Prebuilt Model
The beginAnalyzeHealthcareEntities(Iterable<String> documents) method can be used to analyze healthcare entities, entity data sources, and entity relations in a list of documents.
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add("The patient is a 54-year-old gentleman with a history of progressive angina over "
+ "the past several months.");
}
SyncPoller<AnalyzeHealthcareEntitiesOperationDetail, AnalyzeHealthcareEntitiesPagedIterable>
syncPoller = textAnalyticsClient.beginAnalyzeHealthcareEntities(documents);
syncPoller.waitForCompletion();
AnalyzeHealthcareEntitiesPagedIterable result = syncPoller.getFinalResult();
result.forEach(analyzeHealthcareEntitiesResultCollection -> {
analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
System.out.println("document id = " + healthcareEntitiesResult.getId());
System.out.println("Document entities: ");
AtomicInteger ct = new AtomicInteger();
healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
System.out.printf("\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
healthcareEntity.getConfidenceScore());
IterableStream<EntityDataSource> healthcareEntityDataSources =
healthcareEntity.getDataSources();
if (healthcareEntityDataSources != null) {
healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
"\t\tEntity ID in data source: %s, data source: %s.%n",
healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
}
});
// Healthcare entity relation groups
healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
entityRelation.getRoles().forEach(role -> {
final HealthcareEntity entity = role.getEntity();
System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
entity.getText(), entity.getCategory(), role.getName());
});
System.out.printf("\tRelation confidence score: %f.%n",
entityRelation.getConfidenceScore());
});
});
});
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Summarize text-based content: Document Summarization
Text Analytics client can use Natural Language Understanding (NLU) to summarize lengthy documents. For example, extractive or abstractive summarization. Below you can look at the samples on how to use it.
Extractive summarization
The beginExtractSummary(Iterable<String> documents) method returns a list of extract summaries for the provided list of document.
This method is supported since service API version V2023_04_01.
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
+ " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
+ " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
+ "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
+ " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
+ " (Y) and multilingual (Z). At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code"
+ " as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear,"
+ " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
+ " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
+ " pretrained models that can jointly learn representations to support a broad range of downstream"
+ " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
+ " performance on benchmarks in conversational speech recognition, machine translation, "
+ "conversational question answering, machine reading comprehension, and image captioning. These"
+ " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
+ " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
+ "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
+ "foundational component of this aspiration, if grounded with external knowledge sources in "
+ "the downstream AI tasks.");
}
SyncPoller<ExtractiveSummaryOperationDetail, ExtractiveSummaryPagedIterable> syncPoller =
textAnalyticsClient.beginExtractSummary(documents);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(resultCollection -> {
for (ExtractiveSummaryResult documentResult : resultCollection) {
System.out.println("\tExtracted summary sentences:");
for (ExtractiveSummarySentence extractiveSummarySentence : documentResult.getSentences()) {
System.out.printf(
"\t\t Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
extractiveSummarySentence.getText(), extractiveSummarySentence.getLength(),
extractiveSummarySentence.getOffset(), extractiveSummarySentence.getRankScore());
}
}
});
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Abstractive summarization
The beginAbstractSummary(Iterable<String> documents) method returns a list of abstractive summary for the provided list of document.
This method is supported since service API version V2023_04_01.
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
+ " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
+ " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
+ "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
+ " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
+ " (Y) and multilingual (Z). At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code"
+ " as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear,"
+ " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
+ " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
+ " pretrained models that can jointly learn representations to support a broad range of downstream"
+ " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
+ " performance on benchmarks in conversational speech recognition, machine translation, "
+ "conversational question answering, machine reading comprehension, and image captioning. These"
+ " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
+ " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
+ "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
+ "foundational component of this aspiration, if grounded with external knowledge sources in "
+ "the downstream AI tasks.");
}
SyncPoller<AbstractiveSummaryOperationDetail, AbstractiveSummaryPagedIterable> syncPoller =
textAnalyticsClient.beginAbstractSummary(documents);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(resultCollection -> {
for (AbstractiveSummaryResult documentResult : resultCollection) {
System.out.println("\tAbstractive summary sentences:");
for (AbstractiveSummary summarySentence : documentResult.getSummaries()) {
System.out.printf("\t\t Summary text: %s.%n", summarySentence.getText());
for (AbstractiveSummaryContext abstractiveSummaryContext : summarySentence.getContexts()) {
System.out.printf("\t\t offset: %d, length: %d%n",
abstractiveSummaryContext.getOffset(), abstractiveSummaryContext.getLength());
}
}
}
});
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Classify Text
Text Analytics client can use Natural Language Understanding (NLU) to detect the language or classify the sentiment of text you have. For example, language detection, sentiment analysis, or custom text classification. Below you can look at the samples on how to use it.
Analyze Sentiment and Mine Text for Opinions
The analyzeSentiment(String document, String language, AnalyzeSentimentOptions options) analyzeSentiment} method can be used to analyze sentiment on a given input text string, which returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).
DocumentSentiment documentSentiment = textAnalyticsClient.analyzeSentiment(
"The hotel was dark and unclean.", "en",
new AnalyzeSentimentOptions().setIncludeOpinionMining(true));
for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
sentenceSentiment.getOpinions().forEach(opinion -> {
TargetSentiment targetSentiment = opinion.getTarget();
System.out.printf("\tTarget sentiment: %s, target text: %s%n", targetSentiment.getSentiment(),
targetSentiment.getText());
for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
System.out.printf("\t\t'%s' sentiment because of \"%s\". Is the assessment negated: %s.%n",
assessmentSentiment.getSentiment(), assessmentSentiment.getText(), assessmentSentiment.isNegated());
}
});
}
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Detect Language
The detectLanguage(String document) method returns the detected language and a confidence score between zero and one. Scores close to one indicate 100% certainty that the identified language is true.
This method will use the default country hint that sets up in defaultCountryHint(String countryHint). If none is specified, service will use 'US' as the country hint.
DetectedLanguage detectedLanguage = textAnalyticsClient.detectLanguage("Bonjour tout le monde");
System.out.printf("Detected language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore());
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Single-Label Classification
The beginSingleLabelClassify(Iterable<String> documents, String projectName, String deploymentName) method returns a list of single-label classification for the provided list of documents.
Note: this method is supported since service API version V2022_05_01.
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
+ "in oil and natural gas development on federal lands over the past six years has stretched the"
+ " staff of the BLM to a point that it has been unable to meet its environmental protection "
+ "responsibilities."
);
}
// See the service documentation for regional support and how to train a model to classify your documents,
// see https://aka.ms/azsdk/textanalytics/customfunctionalities
SyncPoller<ClassifyDocumentOperationDetail, ClassifyDocumentPagedIterable> syncPoller =
textAnalyticsClient.beginSingleLabelClassify(documents, "{project_name}", "{deployment_name}");
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (ClassifyDocumentResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (ClassificationCategory classification : documentResult.getClassifications()) {
System.out.printf("\tCategory: %s, confidence score: %f.%n",
classification.getCategory(), classification.getConfidenceScore());
}
}
});
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Multi-Label Classification
The beginMultiLabelClassify(Iterable<String> documents, String projectName, String deploymentName) method returns a list of multi-label classification for the provided list of document.
Note: this method is supported since service API version V2022_05_01.
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"I need a reservation for an indoor restaurant in China. Please don't stop the music."
+ " Play music and add it to my playlist");
}
SyncPoller<ClassifyDocumentOperationDetail, ClassifyDocumentPagedIterable> syncPoller =
textAnalyticsClient.beginMultiLabelClassify(documents, "{project_name}", "{deployment_name}");
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (ClassifyDocumentResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (ClassificationCategory classification : documentResult.getClassifications()) {
System.out.printf("\tCategory: %s, confidence score: %f.%n",
classification.getCategory(), classification.getConfidenceScore());
}
}
});
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Execute multiple actions
The beginAnalyzeActions(Iterable<String> documents, TextAnalyticsActions actions) method execute actions, such as, entities recognition, PII entities recognition, key phrases extraction, and etc, for a list of documents.
List<String> documents = Arrays.asList(
"Elon Musk is the CEO of SpaceX and Tesla.",
"My SSN is 859-98-0987"
);
SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
textAnalyticsClient.beginAnalyzeActions(
documents,
new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
.setRecognizeEntitiesActions(new RecognizeEntitiesAction())
.setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()));
syncPoller.waitForCompletion();
AnalyzeActionsResultPagedIterable result = syncPoller.getFinalResult();
result.forEach(analyzeActionsResult -> {
System.out.println("Entities recognition action results:");
analyzeActionsResult.getRecognizeEntitiesResults().forEach(
actionResult -> {
if (!actionResult.isError()) {
actionResult.getDocumentsResults().forEach(
entitiesResult -> entitiesResult.getEntities().forEach(
entity -> System.out.printf(
"Recognized entity: %s, entity category: %s, entity subcategory: %s,"
+ " confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(),
entity.getConfidenceScore())));
}
});
System.out.println("Key phrases extraction action results:");
analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
actionResult -> {
if (!actionResult.isError()) {
actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
System.out.println("Extracted phrases:");
extractKeyPhraseResult.getKeyPhrases()
.forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
});
}
});
});
See this for supported languages in Text Analytics API.
Note: For asynchronous sample, refer to TextAnalyticsAsyncClient.
Method Summary
Methods inherited from java.lang.Object
Method Details
analyzeSentiment
public DocumentSentiment analyzeSentiment(String document)
Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
Analyze the sentiments of documents
DocumentSentiment documentSentiment =
textAnalyticsClient.analyzeSentiment("The hotel was dark and unclean.");
System.out.printf(
"Recognized sentiment: %s, positive score: %.2f, neutral score: %.2f, negative score: %.2f.%n",
documentSentiment.getSentiment(),
documentSentiment.getConfidenceScores().getPositive(),
documentSentiment.getConfidenceScores().getNeutral(),
documentSentiment.getConfidenceScores().getNegative());
for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
System.out.printf(
"Recognized sentence sentiment: %s, positive score: %.2f, neutral score: %.2f, negative score: %.2f.%n",
sentenceSentiment.getSentiment(),
sentenceSentiment.getConfidenceScores().getPositive(),
sentenceSentiment.getConfidenceScores().getNeutral(),
sentenceSentiment.getConfidenceScores().getNegative());
}
Parameters:
Returns:
analyzeSentiment
public DocumentSentiment analyzeSentiment(String document, String language)
Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.
Code Sample
Analyze the sentiments in a document with a provided language representation.
DocumentSentiment documentSentiment = textAnalyticsClient.analyzeSentiment(
"The hotel was dark and unclean.", "en");
System.out.printf(
"Recognized sentiment: %s, positive score: %.2f, neutral score: %.2f, negative score: %.2f.%n",
documentSentiment.getSentiment(),
documentSentiment.getConfidenceScores().getPositive(),
documentSentiment.getConfidenceScores().getNeutral(),
documentSentiment.getConfidenceScores().getNegative());
for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
System.out.printf(
"Recognized sentence sentiment: %s, positive score: %.2f, neutral score: %.2f, negative score: %.2f.%n",
sentenceSentiment.getSentiment(),
sentenceSentiment.getConfidenceScores().getPositive(),
sentenceSentiment.getConfidenceScores().getNeutral(),
sentenceSentiment.getConfidenceScores().getNegative());
}
Parameters:
Returns:
analyzeSentiment
public DocumentSentiment analyzeSentiment(String document, String language, AnalyzeSentimentOptions options)
Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).
Code Sample
Analyze the sentiment and mine the opinions for each sentence in a document with a provided language representation and AnalyzeSentimentOptions options.
DocumentSentiment documentSentiment = textAnalyticsClient.analyzeSentiment(
"The hotel was dark and unclean.", "en",
new AnalyzeSentimentOptions().setIncludeOpinionMining(true));
for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
sentenceSentiment.getOpinions().forEach(opinion -> {
TargetSentiment targetSentiment = opinion.getTarget();
System.out.printf("\tTarget sentiment: %s, target text: %s%n", targetSentiment.getSentiment(),
targetSentiment.getText());
for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
System.out.printf("\t\t'%s' sentiment because of \"%s\". Is the assessment negated: %s.%n",
assessmentSentiment.getSentiment(), assessmentSentiment.getText(), assessmentSentiment.isNegated());
}
});
}
Parameters:
Returns:
analyzeSentimentBatch
public AnalyzeSentimentResultCollection analyzeSentimentBatch(Iterable
Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).
Code Sample
Analyze the sentiments and mine the opinions for each sentence in a list of documents with a provided language representation and AnalyzeSentimentOptions options.
List<String> documents = Arrays.asList(
"The hotel was dark and unclean. The restaurant had amazing gnocchi.",
"The restaurant had amazing gnocchi. The hotel was dark and unclean."
);
// Analyzing batch sentiments
AnalyzeSentimentResultCollection resultCollection = textAnalyticsClient.analyzeSentimentBatch(
documents, "en", new AnalyzeSentimentOptions().setIncludeOpinionMining(true));
// Analyzed sentiment for each of documents from a batch of documents
resultCollection.forEach(analyzeSentimentResult -> {
System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
documentSentiment.getSentences().forEach(sentenceSentiment -> {
System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
sentenceSentiment.getOpinions().forEach(opinion -> {
TargetSentiment targetSentiment = opinion.getTarget();
System.out.printf("\tTarget sentiment: %s, target text: %s%n", targetSentiment.getSentiment(),
targetSentiment.getText());
for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
System.out.printf("\t\t'%s' sentiment because of \"%s\". Is the assessment negated: %s.%n",
assessmentSentiment.getSentiment(), assessmentSentiment.getText(), assessmentSentiment.isNegated());
}
});
});
});
Parameters:
Returns:
analyzeSentimentBatch
@Deprecated
public AnalyzeSentimentResultCollection analyzeSentimentBatch(Iterable
Deprecated
Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.
Code Sample
Analyze the sentiments in a list of documents with a provided language representation and request options.
List<String> documents = Arrays.asList(
"The hotel was dark and unclean. The restaurant had amazing gnocchi.",
"The restaurant had amazing gnocchi. The hotel was dark and unclean."
);
// Analyzing batch sentiments
AnalyzeSentimentResultCollection resultCollection = textAnalyticsClient.analyzeSentimentBatch(
documents, "en", new TextAnalyticsRequestOptions().setIncludeStatistics(true));
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
// Analyzed sentiment for each of documents from a batch of documents
resultCollection.forEach(analyzeSentimentResult -> {
System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
// Valid document
DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
System.out.printf(
"Recognized document sentiment: %s, positive score: %.2f, neutral score: %.2f,"
+ " negative score: %.2f.%n",
documentSentiment.getSentiment(),
documentSentiment.getConfidenceScores().getPositive(),
documentSentiment.getConfidenceScores().getNeutral(),
documentSentiment.getConfidenceScores().getNegative());
documentSentiment.getSentences().forEach(sentenceSentiment -> System.out.printf(
"Recognized sentence sentiment: %s, positive score: %.2f, neutral score: %.2f,"
+ " negative score: %.2f.%n",
sentenceSentiment.getSentiment(),
sentenceSentiment.getConfidenceScores().getPositive(),
sentenceSentiment.getConfidenceScores().getNeutral(),
sentenceSentiment.getConfidenceScores().getNegative()));
});
Parameters:
Returns:
analyzeSentimentBatchWithResponse
public Response
Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).
Code Sample
Analyze sentiment and mine the opinions for each sentence in a list of TextDocumentInput with provided AnalyzeSentimentOptions options.
List<TextDocumentInput> textDocumentInputs = Arrays.asList(
new TextDocumentInput("1", "The hotel was dark and unclean. The restaurant had amazing gnocchi.")
.setLanguage("en"),
new TextDocumentInput("2", "The restaurant had amazing gnocchi. The hotel was dark and unclean.")
.setLanguage("en")
);
AnalyzeSentimentOptions options = new AnalyzeSentimentOptions().setIncludeOpinionMining(true)
.setIncludeStatistics(true);
// Analyzing batch sentiments
Response<AnalyzeSentimentResultCollection> response =
textAnalyticsClient.analyzeSentimentBatchWithResponse(textDocumentInputs, options, Context.NONE);
// Response's status code
System.out.printf("Status code of request response: %d%n", response.getStatusCode());
AnalyzeSentimentResultCollection resultCollection = response.getValue();
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
// Analyzed sentiment for each of documents from a batch of documents
resultCollection.forEach(analyzeSentimentResult -> {
System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
documentSentiment.getSentences().forEach(sentenceSentiment -> {
System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
sentenceSentiment.getOpinions().forEach(opinion -> {
TargetSentiment targetSentiment = opinion.getTarget();
System.out.printf("\tTarget sentiment: %s, target text: %s%n", targetSentiment.getSentiment(),
targetSentiment.getText());
for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
System.out.printf("\t\t'%s' sentiment because of \"%s\". Is the assessment negated: %s.%n",
assessmentSentiment.getSentiment(), assessmentSentiment.getText(),
assessmentSentiment.isNegated());
}
});
});
});
Parameters:
Returns:
analyzeSentimentBatchWithResponse
@Deprecated
public Response
Deprecated
Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.
Code Sample
Analyze sentiment in a list of TextDocumentInput with provided request options.
List<TextDocumentInput> textDocumentInputs = Arrays.asList(
new TextDocumentInput("1", "The hotel was dark and unclean. The restaurant had amazing gnocchi.")
.setLanguage("en"),
new TextDocumentInput("2", "The restaurant had amazing gnocchi. The hotel was dark and unclean.")
.setLanguage("en")
);
// Analyzing batch sentiments
Response<AnalyzeSentimentResultCollection> response =
textAnalyticsClient.analyzeSentimentBatchWithResponse(textDocumentInputs,
new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
// Response's status code
System.out.printf("Status code of request response: %d%n", response.getStatusCode());
AnalyzeSentimentResultCollection resultCollection = response.getValue();
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
// Analyzed sentiment for each of documents from a batch of documents
resultCollection.forEach(analyzeSentimentResult -> {
System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
// Valid document
DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
System.out.printf(
"Recognized document sentiment: %s, positive score: %.2f, neutral score: %.2f, "
+ "negative score: %.2f.%n",
documentSentiment.getSentiment(),
documentSentiment.getConfidenceScores().getPositive(),
documentSentiment.getConfidenceScores().getNeutral(),
documentSentiment.getConfidenceScores().getNegative());
documentSentiment.getSentences().forEach(sentenceSentiment -> {
System.out.printf(
"Recognized sentence sentiment: %s, positive score: %.2f, neutral score: %.2f,"
+ " negative score: %.2f.%n",
sentenceSentiment.getSentiment(),
sentenceSentiment.getConfidenceScores().getPositive(),
sentenceSentiment.getConfidenceScores().getNeutral(),
sentenceSentiment.getConfidenceScores().getNegative());
});
});
Parameters:
Returns:
beginAbstractSummary
public SyncPoller
Returns a list of abstractive summary for the provided list of TextDocumentInput with provided request options.
This method is supported since service API version V2023_04_01.
Code Sample
List<TextDocumentInput> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(new TextDocumentInput(Integer.toString(i),
"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
+ " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
+ " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
+ "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
+ " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
+ " (Y) and multilingual (Z). At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code"
+ " as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear,"
+ " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
+ " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
+ " pretrained models that can jointly learn representations to support a broad range of downstream"
+ " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
+ " performance on benchmarks in conversational speech recognition, machine translation, "
+ "conversational question answering, machine reading comprehension, and image captioning. These"
+ " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
+ " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
+ "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
+ "foundational component of this aspiration, if grounded with external knowledge sources in "
+ "the downstream AI tasks."));
}
SyncPoller<AbstractiveSummaryOperationDetail, AbstractiveSummaryPagedIterable> syncPoller =
textAnalyticsClient.beginAbstractSummary(documents,
new AbstractiveSummaryOptions().setDisplayName("{tasks_display_name}").setSentenceCount(3),
Context.NONE);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(resultCollection -> {
for (AbstractiveSummaryResult documentResult : resultCollection) {
System.out.println("\tAbstractive summary sentences:");
for (AbstractiveSummary summarySentence : documentResult.getSummaries()) {
System.out.printf("\t\t Summary text: %s.%n", summarySentence.getText());
for (AbstractiveSummaryContext abstractiveSummaryContext : summarySentence.getContexts()) {
System.out.printf("\t\t offset: %d, length: %d%n",
abstractiveSummaryContext.getOffset(), abstractiveSummaryContext.getLength());
}
}
}
});
Parameters:
Returns:
beginAbstractSummary
public SyncPoller
Returns a list of abstractive summary for the provided list of document.
This method is supported since service API version V2023_04_01.
This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
+ " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
+ " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
+ "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
+ " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
+ " (Y) and multilingual (Z). At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code"
+ " as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear,"
+ " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
+ " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
+ " pretrained models that can jointly learn representations to support a broad range of downstream"
+ " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
+ " performance on benchmarks in conversational speech recognition, machine translation, "
+ "conversational question answering, machine reading comprehension, and image captioning. These"
+ " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
+ " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
+ "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
+ "foundational component of this aspiration, if grounded with external knowledge sources in "
+ "the downstream AI tasks.");
}
SyncPoller<AbstractiveSummaryOperationDetail, AbstractiveSummaryPagedIterable> syncPoller =
textAnalyticsClient.beginAbstractSummary(documents);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(resultCollection -> {
for (AbstractiveSummaryResult documentResult : resultCollection) {
System.out.println("\tAbstractive summary sentences:");
for (AbstractiveSummary summarySentence : documentResult.getSummaries()) {
System.out.printf("\t\t Summary text: %s.%n", summarySentence.getText());
for (AbstractiveSummaryContext abstractiveSummaryContext : summarySentence.getContexts()) {
System.out.printf("\t\t offset: %d, length: %d%n",
abstractiveSummaryContext.getOffset(), abstractiveSummaryContext.getLength());
}
}
}
});
Parameters:
Returns:
beginAbstractSummary
public SyncPoller
Returns a list of abstractive summary for the provided list of document with provided request options.
This method is supported since service API version V2022_05_01.
See this supported languages in Language service API.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
+ " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
+ " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
+ "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
+ " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
+ " (Y) and multilingual (Z). At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code"
+ " as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear,"
+ " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
+ " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
+ " pretrained models that can jointly learn representations to support a broad range of downstream"
+ " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
+ " performance on benchmarks in conversational speech recognition, machine translation, "
+ "conversational question answering, machine reading comprehension, and image captioning. These"
+ " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
+ " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
+ "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
+ "foundational component of this aspiration, if grounded with external knowledge sources in "
+ "the downstream AI tasks.");
}
SyncPoller<AbstractiveSummaryOperationDetail, AbstractiveSummaryPagedIterable> syncPoller =
textAnalyticsClient.beginAbstractSummary(documents, "en",
new AbstractiveSummaryOptions().setDisplayName("{tasks_display_name}").setSentenceCount(3));
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(resultCollection -> {
for (AbstractiveSummaryResult documentResult : resultCollection) {
System.out.println("\tAbstractive summary sentences:");
for (AbstractiveSummary summarySentence : documentResult.getSummaries()) {
System.out.printf("\t\t Summary text: %s.%n", summarySentence.getText());
for (AbstractiveSummaryContext abstractiveSummaryContext : summarySentence.getContexts()) {
System.out.printf("\t\t offset: %d, length: %d%n",
abstractiveSummaryContext.getOffset(), abstractiveSummaryContext.getLength());
}
}
}
});
Parameters:
Returns:
beginAnalyzeActions
public SyncPoller
Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list of TextDocumentInput with provided request options. See this supported languages in Language service API.
Code Sample
List<TextDocumentInput> documents = Arrays.asList(
new TextDocumentInput("0", "Elon Musk is the CEO of SpaceX and Tesla.").setLanguage("en"),
new TextDocumentInput("1", "My SSN is 859-98-0987").setLanguage("en")
);
SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
textAnalyticsClient.beginAnalyzeActions(
documents,
new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
.setRecognizeEntitiesActions(new RecognizeEntitiesAction())
.setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
new AnalyzeActionsOptions().setIncludeStatistics(false),
Context.NONE);
syncPoller.waitForCompletion();
AnalyzeActionsResultPagedIterable result = syncPoller.getFinalResult();
result.forEach(analyzeActionsResult -> {
System.out.println("Entities recognition action results:");
analyzeActionsResult.getRecognizeEntitiesResults().forEach(
actionResult -> {
if (!actionResult.isError()) {
actionResult.getDocumentsResults().forEach(
entitiesResult -> entitiesResult.getEntities().forEach(
entity -> System.out.printf(
"Recognized entity: %s, entity category: %s, entity subcategory: %s,"
+ " confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(),
entity.getConfidenceScore())));
}
});
System.out.println("Key phrases extraction action results:");
analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
actionResult -> {
if (!actionResult.isError()) {
actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
System.out.println("Extracted phrases:");
extractKeyPhraseResult.getKeyPhrases()
.forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
});
}
});
});
Parameters:
Returns:
beginAnalyzeActions
public SyncPoller
Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list of documents. This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
List<String> documents = Arrays.asList(
"Elon Musk is the CEO of SpaceX and Tesla.",
"My SSN is 859-98-0987"
);
SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
textAnalyticsClient.beginAnalyzeActions(
documents,
new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
.setRecognizeEntitiesActions(new RecognizeEntitiesAction())
.setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()));
syncPoller.waitForCompletion();
AnalyzeActionsResultPagedIterable result = syncPoller.getFinalResult();
result.forEach(analyzeActionsResult -> {
System.out.println("Entities recognition action results:");
analyzeActionsResult.getRecognizeEntitiesResults().forEach(
actionResult -> {
if (!actionResult.isError()) {
actionResult.getDocumentsResults().forEach(
entitiesResult -> entitiesResult.getEntities().forEach(
entity -> System.out.printf(
"Recognized entity: %s, entity category: %s, entity subcategory: %s,"
+ " confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(),
entity.getConfidenceScore())));
}
});
System.out.println("Key phrases extraction action results:");
analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
actionResult -> {
if (!actionResult.isError()) {
actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
System.out.println("Extracted phrases:");
extractKeyPhraseResult.getKeyPhrases()
.forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
});
}
});
});
Parameters:
Returns:
beginAnalyzeActions
public SyncPoller
Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list of documents with provided request options. See this supported languages in Language service API.
Code Sample
List<String> documents = Arrays.asList(
"Elon Musk is the CEO of SpaceX and Tesla.",
"My SSN is 859-98-0987"
);
SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
textAnalyticsClient.beginAnalyzeActions(
documents,
new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
.setRecognizeEntitiesActions(new RecognizeEntitiesAction())
.setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
"en",
new AnalyzeActionsOptions().setIncludeStatistics(false));
syncPoller.waitForCompletion();
AnalyzeActionsResultPagedIterable result = syncPoller.getFinalResult();
result.forEach(analyzeActionsResult -> {
System.out.println("Entities recognition action results:");
analyzeActionsResult.getRecognizeEntitiesResults().forEach(
actionResult -> {
if (!actionResult.isError()) {
actionResult.getDocumentsResults().forEach(
entitiesResult -> entitiesResult.getEntities().forEach(
entity -> System.out.printf(
"Recognized entity: %s, entity category: %s, entity subcategory: %s,"
+ " confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(),
entity.getConfidenceScore())));
}
});
System.out.println("Key phrases extraction action results:");
analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
actionResult -> {
if (!actionResult.isError()) {
actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
System.out.println("Extracted phrases:");
extractKeyPhraseResult.getKeyPhrases()
.forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
});
}
});
});
Parameters:
Returns:
beginAnalyzeHealthcareEntities
public SyncPoller
Analyze healthcare entities, entity data sources, and entity relations in a list of TextDocumentInput and provided request options to show statistics. See this supported languages in Language service API.
Code Sample
List<TextDocumentInput> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(new TextDocumentInput(Integer.toString(i),
"The patient is a 54-year-old gentleman with a history of progressive angina over "
+ "the past several months."));
}
// Request options: show statistics and model version
AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions()
.setIncludeStatistics(true);
SyncPoller<AnalyzeHealthcareEntitiesOperationDetail, AnalyzeHealthcareEntitiesPagedIterable>
syncPoller = textAnalyticsClient.beginAnalyzeHealthcareEntities(documents, options, Context.NONE);
syncPoller.waitForCompletion();
AnalyzeHealthcareEntitiesPagedIterable result = syncPoller.getFinalResult();
// Task operation statistics
AnalyzeHealthcareEntitiesOperationDetail operationResult = syncPoller.poll().getValue();
System.out.printf("Operation created time: %s, expiration time: %s.%n",
operationResult.getCreatedAt(), operationResult.getExpiresAt());
result.forEach(analyzeHealthcareEntitiesResultCollection -> {
// Model version
System.out.printf("Results of Azure Text Analytics \"Analyze Healthcare\" Model, version: %s%n",
analyzeHealthcareEntitiesResultCollection.getModelVersion());
TextDocumentBatchStatistics healthcareTaskStatistics =
analyzeHealthcareEntitiesResultCollection.getStatistics();
// Batch statistics
System.out.printf("Documents statistics: document count = %d, erroneous document count = %d,"
+ " transaction count = %d, valid document count = %d.%n",
healthcareTaskStatistics.getDocumentCount(), healthcareTaskStatistics.getInvalidDocumentCount(),
healthcareTaskStatistics.getTransactionCount(), healthcareTaskStatistics.getValidDocumentCount());
analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
System.out.println("document id = " + healthcareEntitiesResult.getId());
System.out.println("Document entities: ");
AtomicInteger ct = new AtomicInteger();
healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
System.out.printf("\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
healthcareEntity.getConfidenceScore());
IterableStream<EntityDataSource> healthcareEntityDataSources =
healthcareEntity.getDataSources();
if (healthcareEntityDataSources != null) {
healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
"\t\tEntity ID in data source: %s, data source: %s.%n",
healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
}
});
// Healthcare entity relation groups
healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
entityRelation.getRoles().forEach(role -> {
final HealthcareEntity entity = role.getEntity();
System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
entity.getText(), entity.getCategory(), role.getName());
});
System.out.printf("\tRelation confidence score: %f.%n", entityRelation.getConfidenceScore());
});
});
});
Parameters:
Returns:
beginAnalyzeHealthcareEntities
public SyncPoller
Analyze healthcare entities, entity data sources, and entity relations in a list of documents. This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add("The patient is a 54-year-old gentleman with a history of progressive angina over "
+ "the past several months.");
}
SyncPoller<AnalyzeHealthcareEntitiesOperationDetail, AnalyzeHealthcareEntitiesPagedIterable>
syncPoller = textAnalyticsClient.beginAnalyzeHealthcareEntities(documents);
syncPoller.waitForCompletion();
AnalyzeHealthcareEntitiesPagedIterable result = syncPoller.getFinalResult();
result.forEach(analyzeHealthcareEntitiesResultCollection -> {
analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
System.out.println("document id = " + healthcareEntitiesResult.getId());
System.out.println("Document entities: ");
AtomicInteger ct = new AtomicInteger();
healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
System.out.printf("\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
healthcareEntity.getConfidenceScore());
IterableStream<EntityDataSource> healthcareEntityDataSources =
healthcareEntity.getDataSources();
if (healthcareEntityDataSources != null) {
healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
"\t\tEntity ID in data source: %s, data source: %s.%n",
healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
}
});
// Healthcare entity relation groups
healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
entityRelation.getRoles().forEach(role -> {
final HealthcareEntity entity = role.getEntity();
System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
entity.getText(), entity.getCategory(), role.getName());
});
System.out.printf("\tRelation confidence score: %f.%n",
entityRelation.getConfidenceScore());
});
});
});
Parameters:
Returns:
beginAnalyzeHealthcareEntities
public SyncPoller
Analyze healthcare entities, entity data sources, and entity relations in a list of documents with provided request options. See this supported languages in Language service API.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add("The patient is a 54-year-old gentleman with a history of progressive angina over "
+ "the past several months.");
}
// Request options: show statistics and model version
AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions()
.setIncludeStatistics(true);
SyncPoller<AnalyzeHealthcareEntitiesOperationDetail, AnalyzeHealthcareEntitiesPagedIterable>
syncPoller = textAnalyticsClient.beginAnalyzeHealthcareEntities(documents, "en", options);
syncPoller.waitForCompletion();
AnalyzeHealthcareEntitiesPagedIterable result = syncPoller.getFinalResult();
result.forEach(analyzeHealthcareEntitiesResultCollection -> {
// Model version
System.out.printf("Results of Azure Text Analytics \"Analyze Healthcare\" Model, version: %s%n",
analyzeHealthcareEntitiesResultCollection.getModelVersion());
TextDocumentBatchStatistics healthcareTaskStatistics =
analyzeHealthcareEntitiesResultCollection.getStatistics();
// Batch statistics
System.out.printf("Documents statistics: document count = %d, erroneous document count = %d,"
+ " transaction count = %d, valid document count = %d.%n",
healthcareTaskStatistics.getDocumentCount(), healthcareTaskStatistics.getInvalidDocumentCount(),
healthcareTaskStatistics.getTransactionCount(), healthcareTaskStatistics.getValidDocumentCount());
analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
System.out.println("document id = " + healthcareEntitiesResult.getId());
System.out.println("Document entities: ");
AtomicInteger ct = new AtomicInteger();
healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
System.out.printf("\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
healthcareEntity.getConfidenceScore());
IterableStream<EntityDataSource> healthcareEntityDataSources =
healthcareEntity.getDataSources();
if (healthcareEntityDataSources != null) {
healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
"\t\tEntity ID in data source: %s, data source: %s.%n",
healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
}
});
// Healthcare entity relation groups
healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
entityRelation.getRoles().forEach(role -> {
final HealthcareEntity entity = role.getEntity();
System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
entity.getText(), entity.getCategory(), role.getName());
});
System.out.printf("\tRelation confidence score: %f.%n", entityRelation.getConfidenceScore());
});
});
});
Parameters:
Returns:
beginExtractSummary
public SyncPoller
Returns a list of extract summaries for the provided list of TextDocumentInput with provided request options.
This method is supported since service API version V2023_04_01.
Code Sample
List<TextDocumentInput> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(new TextDocumentInput(Integer.toString(i),
"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
+ " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
+ " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
+ "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
+ " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
+ " (Y) and multilingual (Z). At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code"
+ " as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear,"
+ " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
+ " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
+ " pretrained models that can jointly learn representations to support a broad range of downstream"
+ " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
+ " performance on benchmarks in conversational speech recognition, machine translation, "
+ "conversational question answering, machine reading comprehension, and image captioning. These"
+ " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
+ " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
+ "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
+ "foundational component of this aspiration, if grounded with external knowledge sources in "
+ "the downstream AI tasks."));
}
SyncPoller<ExtractiveSummaryOperationDetail, ExtractiveSummaryPagedIterable> syncPoller =
textAnalyticsClient.beginExtractSummary(documents,
new ExtractiveSummaryOptions().setMaxSentenceCount(4).setOrderBy(ExtractiveSummarySentencesOrder.RANK),
Context.NONE);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(resultCollection -> {
for (ExtractiveSummaryResult documentResult : resultCollection) {
System.out.println("\tExtracted summary sentences:");
for (ExtractiveSummarySentence extractiveSummarySentence : documentResult.getSentences()) {
System.out.printf(
"\t\t Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
extractiveSummarySentence.getText(), extractiveSummarySentence.getLength(),
extractiveSummarySentence.getOffset(), extractiveSummarySentence.getRankScore());
}
}
});
Parameters:
Returns:
beginExtractSummary
public SyncPoller
Returns a list of extract summaries for the provided list of document.
This method is supported since service API version V2023_04_01.
This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
+ " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
+ " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
+ "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
+ " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
+ " (Y) and multilingual (Z). At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code"
+ " as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear,"
+ " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
+ " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
+ " pretrained models that can jointly learn representations to support a broad range of downstream"
+ " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
+ " performance on benchmarks in conversational speech recognition, machine translation, "
+ "conversational question answering, machine reading comprehension, and image captioning. These"
+ " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
+ " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
+ "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
+ "foundational component of this aspiration, if grounded with external knowledge sources in "
+ "the downstream AI tasks.");
}
SyncPoller<ExtractiveSummaryOperationDetail, ExtractiveSummaryPagedIterable> syncPoller =
textAnalyticsClient.beginExtractSummary(documents);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(resultCollection -> {
for (ExtractiveSummaryResult documentResult : resultCollection) {
System.out.println("\tExtracted summary sentences:");
for (ExtractiveSummarySentence extractiveSummarySentence : documentResult.getSentences()) {
System.out.printf(
"\t\t Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
extractiveSummarySentence.getText(), extractiveSummarySentence.getLength(),
extractiveSummarySentence.getOffset(), extractiveSummarySentence.getRankScore());
}
}
});
Parameters:
Returns:
beginExtractSummary
public SyncPoller
Returns a list of extract summaries for the provided list of document with provided request options.
This method is supported since service API version V2023_04_01.
See this supported languages in Language service API.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic,"
+ " human-centric approach to learning and understanding. As Chief Technology Officer of Azure AI"
+ " Cognitive Services, I have been working with a team of amazing scientists and engineers to turn "
+ "this quest into a reality. In my role, I enjoy a unique perspective in viewing the relationship"
+ " among three attributes of human cognition: monolingual text (X), audio or visual sensory signals,"
+ " (Y) and multilingual (Z). At the intersection of all three, there\u2019s magic\u2014what we call XYZ-code"
+ " as illustrated in Figure 1\u2014a joint representation to create more powerful AI that can speak, hear,"
+ " see, and understand humans better. We believe XYZ-code will enable us to fulfill our long-term"
+ " vision: cross-domain transfer learning, spanning modalities and languages. The goal is to have"
+ " pretrained models that can jointly learn representations to support a broad range of downstream"
+ " AI tasks, much in the way humans do today. Over the past five years, we have achieved human"
+ " performance on benchmarks in conversational speech recognition, machine translation, "
+ "conversational question answering, machine reading comprehension, and image captioning. These"
+ " five breakthroughs provided us with strong signals toward our more ambitious aspiration to"
+ " produce a leap in AI capabilities, achieving multisensory and multilingual learning that "
+ "is closer in line with how humans learn and understand. I believe the joint XYZ-code is a "
+ "foundational component of this aspiration, if grounded with external knowledge sources in "
+ "the downstream AI tasks.");
}
SyncPoller<ExtractiveSummaryOperationDetail, ExtractiveSummaryPagedIterable> syncPoller =
textAnalyticsClient.beginExtractSummary(documents,
"en",
new ExtractiveSummaryOptions().setMaxSentenceCount(4).setOrderBy(ExtractiveSummarySentencesOrder.RANK));
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(resultCollection -> {
for (ExtractiveSummaryResult documentResult : resultCollection) {
System.out.println("\tExtracted summary sentences:");
for (ExtractiveSummarySentence extractiveSummarySentence : documentResult.getSentences()) {
System.out.printf(
"\t\t Sentence text: %s, length: %d, offset: %d, rank score: %f.%n",
extractiveSummarySentence.getText(), extractiveSummarySentence.getLength(),
extractiveSummarySentence.getOffset(), extractiveSummarySentence.getRankScore());
}
}
});
Parameters:
Returns:
beginMultiLabelClassify
public SyncPoller
Returns a list of multi-label classification for the provided list of TextDocumentInput with provided request options.
This method is supported since service API version V2022_05_01.
Code Sample
List<TextDocumentInput> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(new TextDocumentInput(Integer.toString(i),
"I need a reservation for an indoor restaurant in China. Please don't stop the music."
+ " Play music and add it to my playlist"));
}
MultiLabelClassifyOptions options = new MultiLabelClassifyOptions().setIncludeStatistics(true);
SyncPoller<ClassifyDocumentOperationDetail, ClassifyDocumentPagedIterable> syncPoller =
textAnalyticsClient.beginMultiLabelClassify(documents, "{project_name}", "{deployment_name}",
options, Context.NONE);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (ClassifyDocumentResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (ClassificationCategory classification : documentResult.getClassifications()) {
System.out.printf("\tCategory: %s, confidence score: %f.%n",
classification.getCategory(), classification.getConfidenceScore());
}
}
});
Parameters:
Returns:
beginMultiLabelClassify
public SyncPoller
Returns a list of multi-label classification for the provided list of document.
This method is supported since service API version V2022_05_01.
This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"I need a reservation for an indoor restaurant in China. Please don't stop the music."
+ " Play music and add it to my playlist");
}
SyncPoller<ClassifyDocumentOperationDetail, ClassifyDocumentPagedIterable> syncPoller =
textAnalyticsClient.beginMultiLabelClassify(documents, "{project_name}", "{deployment_name}");
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (ClassifyDocumentResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (ClassificationCategory classification : documentResult.getClassifications()) {
System.out.printf("\tCategory: %s, confidence score: %f.%n",
classification.getCategory(), classification.getConfidenceScore());
}
}
});
Parameters:
Returns:
beginMultiLabelClassify
public SyncPoller
Returns a list of multi-label classification for the provided list of document with provided request options.
This method is supported since service API version V2022_05_01.
See this supported languages in Language service API.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"I need a reservation for an indoor restaurant in China. Please don't stop the music."
+ " Play music and add it to my playlist");
}
MultiLabelClassifyOptions options = new MultiLabelClassifyOptions().setIncludeStatistics(true);
SyncPoller<ClassifyDocumentOperationDetail, ClassifyDocumentPagedIterable> syncPoller =
textAnalyticsClient.beginMultiLabelClassify(documents, "{project_name}", "{deployment_name}", "en", options);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (ClassifyDocumentResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (ClassificationCategory classification : documentResult.getClassifications()) {
System.out.printf("\tCategory: %s, confidence score: %f.%n",
classification.getCategory(), classification.getConfidenceScore());
}
}
});
Parameters:
Returns:
beginRecognizeCustomEntities
public SyncPoller
Returns a list of custom entities for the provided list of TextDocumentInput with provided request options.
This method is supported since service API version V2022_05_01
See this supported languages in Language service API.
Code Sample
List<TextDocumentInput> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(new TextDocumentInput(Integer.toString(i),
"A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
+ "in oil and natural gas development on federal lands over the past six years has stretched the"
+ " staff of the BLM to a point that it has been unable to meet its environmental protection "
+ "responsibilities."));
RecognizeCustomEntitiesOptions options = new RecognizeCustomEntitiesOptions().setIncludeStatistics(true);
SyncPoller<RecognizeCustomEntitiesOperationDetail, RecognizeCustomEntitiesPagedIterable> syncPoller =
textAnalyticsClient.beginRecognizeCustomEntities(documents, "{project_name}",
"{deployment_name}", options, Context.NONE);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (RecognizeEntitiesResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (CategorizedEntity entity : documentResult.getEntities()) {
System.out.printf(
"\tText: %s, category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore());
}
}
});
}
Parameters:
Returns:
beginRecognizeCustomEntities
public SyncPoller
Returns a list of custom entities for the provided list of document.
This method is supported since service API version V2022_05_01.
This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
+ "in oil and natural gas development on federal lands over the past six years has stretched the"
+ " staff of the BLM to a point that it has been unable to meet its environmental protection "
+ "responsibilities."); }
SyncPoller<RecognizeCustomEntitiesOperationDetail, RecognizeCustomEntitiesPagedIterable> syncPoller =
textAnalyticsClient.beginRecognizeCustomEntities(documents, "{project_name}", "{deployment_name}");
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (RecognizeEntitiesResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (CategorizedEntity entity : documentResult.getEntities()) {
System.out.printf(
"\tText: %s, category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore());
}
}
});
Parameters:
Returns:
beginRecognizeCustomEntities
public SyncPoller
Returns a list of custom entities for the provided list of document with provided request options.
This method is supported since service API version V2022_05_01.
See this supported languages in Language service API.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
+ "in oil and natural gas development on federal lands over the past six years has stretched the"
+ " staff of the BLM to a point that it has been unable to meet its environmental protection "
+ "responsibilities."); }
RecognizeCustomEntitiesOptions options = new RecognizeCustomEntitiesOptions().setIncludeStatistics(true);
SyncPoller<RecognizeCustomEntitiesOperationDetail, RecognizeCustomEntitiesPagedIterable> syncPoller =
textAnalyticsClient.beginRecognizeCustomEntities(documents, "{project_name}",
"{deployment_name}", "en", options);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (RecognizeEntitiesResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (CategorizedEntity entity : documentResult.getEntities()) {
System.out.printf(
"\tText: %s, category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore());
}
}
});
Parameters:
Returns:
beginSingleLabelClassify
public SyncPoller
Returns a list of single-label classification for the provided list of TextDocumentInput with provided request options.
This method is supported since service API version V2022_05_01.
Code Sample
List<TextDocumentInput> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(new TextDocumentInput(Integer.toString(i),
"A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
+ "in oil and natural gas development on federal lands over the past six years has stretched the"
+ " staff of the BLM to a point that it has been unable to meet its environmental protection "
+ "responsibilities."));
}
SingleLabelClassifyOptions options = new SingleLabelClassifyOptions().setIncludeStatistics(true);
// See the service documentation for regional support and how to train a model to classify your documents,
// see https://aka.ms/azsdk/textanalytics/customfunctionalities
SyncPoller<ClassifyDocumentOperationDetail, ClassifyDocumentPagedIterable> syncPoller =
textAnalyticsClient.beginSingleLabelClassify(documents, "{project_name}", "{deployment_name}",
options, Context.NONE);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (ClassifyDocumentResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (ClassificationCategory classification : documentResult.getClassifications()) {
System.out.printf("\tCategory: %s, confidence score: %f.%n",
classification.getCategory(), classification.getConfidenceScore());
}
}
});
Parameters:
Returns:
beginSingleLabelClassify
public SyncPoller
Returns a list of single-label classification for the provided list of document.
This method is supported since service API version V2022_05_01.
This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
+ "in oil and natural gas development on federal lands over the past six years has stretched the"
+ " staff of the BLM to a point that it has been unable to meet its environmental protection "
+ "responsibilities."
);
}
// See the service documentation for regional support and how to train a model to classify your documents,
// see https://aka.ms/azsdk/textanalytics/customfunctionalities
SyncPoller<ClassifyDocumentOperationDetail, ClassifyDocumentPagedIterable> syncPoller =
textAnalyticsClient.beginSingleLabelClassify(documents, "{project_name}", "{deployment_name}");
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (ClassifyDocumentResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (ClassificationCategory classification : documentResult.getClassifications()) {
System.out.printf("\tCategory: %s, confidence score: %f.%n",
classification.getCategory(), classification.getConfidenceScore());
}
}
});
Parameters:
Returns:
beginSingleLabelClassify
public SyncPoller
Returns a list of single-label classification for the provided list of document with provided request options.
This method is supported since service API version V2022_05_01.
See this supported languages in Language service API.
Code Sample
List<String> documents = new ArrayList<>();
for (int i = 0; i < 3; i++) {
documents.add(
"A recent report by the Government Accountability Office (GAO) found that the dramatic increase "
+ "in oil and natural gas development on federal lands over the past six years has stretched the"
+ " staff of the BLM to a point that it has been unable to meet its environmental protection "
+ "responsibilities."
);
}
SingleLabelClassifyOptions options = new SingleLabelClassifyOptions().setIncludeStatistics(true);
// See the service documentation for regional support and how to train a model to classify your documents,
// see https://aka.ms/azsdk/textanalytics/customfunctionalities
SyncPoller<ClassifyDocumentOperationDetail, ClassifyDocumentPagedIterable> syncPoller =
textAnalyticsClient.beginSingleLabelClassify(documents, "{project_name}", "{deployment_name}",
"en", options);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(documentsResults -> {
System.out.printf("Project name: %s, deployment name: %s.%n",
documentsResults.getProjectName(), documentsResults.getDeploymentName());
for (ClassifyDocumentResult documentResult : documentsResults) {
System.out.println("Document ID: " + documentResult.getId());
for (ClassificationCategory classification : documentResult.getClassifications()) {
System.out.printf("\tCategory: %s, confidence score: %f.%n",
classification.getCategory(), classification.getConfidenceScore());
}
}
});
Parameters:
Returns:
detectLanguage
public DetectedLanguage detectLanguage(String document)
Returns the detected language and a confidence score between zero and one. Scores close to one indicate 100% certainty that the identified language is true. This method will use the default country hint that sets up in defaultCountryHint(String countryHint). If none is specified, service will use 'US' as the country hint.
Code Sample
Detects the language of single document.
DetectedLanguage detectedLanguage = textAnalyticsClient.detectLanguage("Bonjour tout le monde");
System.out.printf("Detected language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore());
Parameters:
Returns:
detectLanguage
public DetectedLanguage detectLanguage(String document, String countryHint)
Returns the detected language and a confidence score between zero and one. Scores close to one indicate 100% certainty that the identified language is true.
Code Sample
Detects the language of documents with a provided country hint.
DetectedLanguage detectedLanguage = textAnalyticsClient.detectLanguage(
"This text is in English", "US");
System.out.printf("Detected language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore());
Parameters:
countryHint = "" or "none".
Returns:
detectLanguageBatch
public DetectLanguageResultCollection detectLanguageBatch(Iterable
Detects Language for a batch of document with the provided country hint and request options.
Code Sample
Detects the language in a list of documents with a provided country hint and request options.
List<String> documents = Arrays.asList(
"This is written in English",
"Este es un documento escrito en Espa�ol."
);
DetectLanguageResultCollection resultCollection =
textAnalyticsClient.detectLanguageBatch(documents, "US", null);
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
// Batch result of languages
resultCollection.forEach(detectLanguageResult -> {
System.out.printf("Document ID: %s%n", detectLanguageResult.getId());
DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
System.out.printf("Primary language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
detectedLanguage.getName(), detectedLanguage.getIso6391Name(),
detectedLanguage.getConfidenceScore());
});
Parameters:
countryHint = "" or "none".
Returns:
detectLanguageBatchWithResponse
public Response
Detects Language for a batch of DetectLanguageInput with provided request options.
Code Sample
Detects the languages with http response in a list of DetectLanguageInput with provided request options.
List<DetectLanguageInput> detectLanguageInputs = Arrays.asList(
new DetectLanguageInput("1", "This is written in English.", "US"),
new DetectLanguageInput("2", "Este es un documento escrito en Espa�ol.", "es")
);
Response<DetectLanguageResultCollection> response =
textAnalyticsClient.detectLanguageBatchWithResponse(detectLanguageInputs,
new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
// Response's status code
System.out.printf("Status code of request response: %d%n", response.getStatusCode());
DetectLanguageResultCollection detectedLanguageResultCollection = response.getValue();
// Batch statistics
TextDocumentBatchStatistics batchStatistics = detectedLanguageResultCollection.getStatistics();
System.out.printf(
"Documents statistics: document count = %d, erroneous document count = %d, transaction count = %d,"
+ " valid document count = %d.%n",
batchStatistics.getDocumentCount(), batchStatistics.getInvalidDocumentCount(),
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
// Batch result of languages
detectedLanguageResultCollection.forEach(detectLanguageResult -> {
System.out.printf("Document ID: %s%n", detectLanguageResult.getId());
DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
System.out.printf("Primary language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
detectedLanguage.getName(), detectedLanguage.getIso6391Name(),
detectedLanguage.getConfidenceScore());
});
Parameters:
Returns:
extractKeyPhrases
public KeyPhrasesCollection extractKeyPhrases(String document)
Returns a list of strings denoting the key phrases in the document. This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
Extracts key phrases of documents
KeyPhrasesCollection extractedKeyPhrases =
textAnalyticsClient.extractKeyPhrases("My cat might need to see a veterinarian.");
for (String keyPhrase : extractedKeyPhrases) {
System.out.printf("%s.%n", keyPhrase);
}
Parameters:
Returns:
extractKeyPhrases
public KeyPhrasesCollection extractKeyPhrases(String document, String language)
Returns a list of strings denoting the key phrases in the document. See this for the list of enabled languages.
Code Sample
Extracts key phrases in a document with a provided language representation.
textAnalyticsClient.extractKeyPhrases("My cat might need to see a veterinarian.", "en")
.forEach(kegPhrase -> System.out.printf("%s.%n", kegPhrase));
Parameters:
Returns:
extractKeyPhrasesBatch
public ExtractKeyPhrasesResultCollection extractKeyPhrasesBatch(Iterable
Returns a list of strings denoting the key phrases in the documents with provided language code and request options. See this for the list of enabled languages.
Code Sample
Extracts key phrases in a list of documents with a provided language code and request options.
List<String> documents = Arrays.asList(
"My cat might need to see a veterinarian.",
"The pitot tube is used to measure airspeed."
);
// Extracting batch key phrases
ExtractKeyPhrasesResultCollection resultCollection =
textAnalyticsClient.extractKeyPhrasesBatch(documents, "en", null);
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf(
"A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
// Extracted key phrase for each of documents from a batch of documents
resultCollection.forEach(extractKeyPhraseResult -> {
System.out.printf("Document ID: %s%n", extractKeyPhraseResult.getId());
// Valid document
System.out.println("Extracted phrases:");
extractKeyPhraseResult.getKeyPhrases().forEach(keyPhrase -> System.out.printf("%s.%n", keyPhrase));
});
Parameters:
Returns:
extractKeyPhrasesBatchWithResponse
public Response
Returns a list of strings denoting the key phrases in the a batch of TextDocumentInput with request options. See this for the list of enabled languages.
Code Sample
Extracts key phrases with http response in a list of TextDocumentInput with request options.
List<TextDocumentInput> textDocumentInputs = Arrays.asList(
new TextDocumentInput("1", "My cat might need to see a veterinarian.").setLanguage("en"),
new TextDocumentInput("2", "The pitot tube is used to measure airspeed.").setLanguage("en")
);
// Extracting batch key phrases
Response<ExtractKeyPhrasesResultCollection> response =
textAnalyticsClient.extractKeyPhrasesBatchWithResponse(textDocumentInputs,
new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
// Response's status code
System.out.printf("Status code of request response: %d%n", response.getStatusCode());
ExtractKeyPhrasesResultCollection resultCollection = response.getValue();
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf(
"A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
// Extracted key phrase for each of documents from a batch of documents
resultCollection.forEach(extractKeyPhraseResult -> {
System.out.printf("Document ID: %s%n", extractKeyPhraseResult.getId());
// Valid document
System.out.println("Extracted phrases:");
extractKeyPhraseResult.getKeyPhrases().forEach(keyPhrase ->
System.out.printf("%s.%n", keyPhrase));
});
Parameters:
Returns:
getDefaultCountryHint
public String getDefaultCountryHint()
Gets default country hint code.
Returns:
getDefaultLanguage
public String getDefaultLanguage()
Gets default language when the builder is setup.
Returns:
recognizeEntities
public CategorizedEntityCollection recognizeEntities(String document)
Returns a list of general categorized entities in the provided document. For a list of supported entity types, check: this This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
Recognize the entities of documents
CategorizedEntityCollection recognizeEntitiesResult =
textAnalyticsClient.recognizeEntities("Satya Nadella is the CEO of Microsoft");
for (CategorizedEntity entity : recognizeEntitiesResult) {
System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore());
}
Parameters:
Returns:
recognizeEntities
public CategorizedEntityCollection recognizeEntities(String document, String language)
Returns a list of general categorized entities in the provided document with provided language code. For a list of supported entity types, check: this For a list of enabled languages, check: this
Code Sample
Recognizes the entities in a document with a provided language code.
CategorizedEntityCollection recognizeEntitiesResult =
textAnalyticsClient.recognizeEntities("Satya Nadella is the CEO of Microsoft", "en");
for (CategorizedEntity entity : recognizeEntitiesResult) {
System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore());
}
Parameters:
Returns:
recognizeEntitiesBatch
public RecognizeEntitiesResultCollection recognizeEntitiesBatch(Iterable
Returns a list of general categorized entities for the provided list of documents with provided language code and request options.
Code Sample
Recognizes the entities in a list of documents with a provided language code and request options.
List<String> documents = Arrays.asList(
"I had a wonderful trip to Seattle last week.",
"I work at Microsoft.");
RecognizeEntitiesResultCollection resultCollection =
textAnalyticsClient.recognizeEntitiesBatch(documents, "en", null);
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf(
"A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
resultCollection.forEach(recognizeEntitiesResult ->
recognizeEntitiesResult.getEntities().forEach(entity ->
System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore())));
Parameters:
Returns:
recognizeEntitiesBatchWithResponse
public Response
Returns a list of general categorized entities for the provided list of TextDocumentInput with provided request options.
Code Sample
Recognizes the entities with http response in a list of TextDocumentInput with provided request options.
List<TextDocumentInput> textDocumentInputs = Arrays.asList(
new TextDocumentInput("0", "I had a wonderful trip to Seattle last week.").setLanguage("en"),
new TextDocumentInput("1", "I work at Microsoft.").setLanguage("en")
);
Response<RecognizeEntitiesResultCollection> response =
textAnalyticsClient.recognizeEntitiesBatchWithResponse(textDocumentInputs,
new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
// Response's status code
System.out.printf("Status code of request response: %d%n", response.getStatusCode());
RecognizeEntitiesResultCollection recognizeEntitiesResultCollection = response.getValue();
// Batch statistics
TextDocumentBatchStatistics batchStatistics = recognizeEntitiesResultCollection.getStatistics();
System.out.printf(
"A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
recognizeEntitiesResultCollection.forEach(recognizeEntitiesResult ->
recognizeEntitiesResult.getEntities().forEach(entity ->
System.out.printf("Recognized entity: %s, entity category: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getConfidenceScore())));
Parameters:
Returns:
recognizeLinkedEntities
public LinkedEntityCollection recognizeLinkedEntities(String document)
Returns a list of recognized entities with links to a well-known knowledge base for the provided document. See this for supported languages in Text Analytics API. This method will use the default language that can be set by using method defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
Recognize the linked entities of documents
String document = "Old Faithful is a geyser at Yellowstone Park.";
System.out.println("Linked Entities:");
textAnalyticsClient.recognizeLinkedEntities(document).forEach(linkedEntity -> {
System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
linkedEntity.getDataSource());
linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
"Matched entity: %s, confidence score: %f.%n",
entityMatch.getText(), entityMatch.getConfidenceScore()));
});
Parameters:
Returns:
recognizeLinkedEntities
public LinkedEntityCollection recognizeLinkedEntities(String document, String language)
Returns a list of recognized entities with links to a well-known knowledge base for the provided document with language code. See this for supported languages in Text Analytics API.
Code Sample
Recognizes the linked entities in a document with a provided language code.
String document = "Old Faithful is a geyser at Yellowstone Park.";
textAnalyticsClient.recognizeLinkedEntities(document, "en").forEach(linkedEntity -> {
System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
linkedEntity.getDataSource());
linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
"Matched entity: %s, confidence score: %f.%n",
entityMatch.getText(), entityMatch.getConfidenceScore()));
});
Parameters:
Returns:
recognizeLinkedEntitiesBatch
public RecognizeLinkedEntitiesResultCollection recognizeLinkedEntitiesBatch(Iterable
Returns a list of recognized entities with links to a well-known knowledge base for the list of documents with provided language code and request options. See this for supported languages in Text Analytics API.
Code Sample
Recognizes the linked entities in a list of documents with a provided language code and request options.
List<String> documents = Arrays.asList(
"Old Faithful is a geyser at Yellowstone Park.",
"Mount Shasta has lenticular clouds."
);
RecognizeLinkedEntitiesResultCollection resultCollection =
textAnalyticsClient.recognizeLinkedEntitiesBatch(documents, "en", null);
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
resultCollection.forEach(recognizeLinkedEntitiesResult ->
recognizeLinkedEntitiesResult.getEntities().forEach(linkedEntity -> {
System.out.println("Linked Entities:");
System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
linkedEntity.getDataSource());
linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
"Matched entity: %s, confidence score: %f.%n",
entityMatch.getText(), entityMatch.getConfidenceScore()));
}));
Parameters:
Returns:
recognizeLinkedEntitiesBatchWithResponse
public Response
Returns a list of recognized entities with links to a well-known knowledge base for the list of TextDocumentInput and request options. See this for supported languages in Text Analytics API.
Code Sample
Recognizes the linked entities with http response in a list of TextDocumentInput with request options.
List<TextDocumentInput> textDocumentInputs = Arrays.asList(
new TextDocumentInput("1", "Old Faithful is a geyser at Yellowstone Park.").setLanguage("en"),
new TextDocumentInput("2", "Mount Shasta has lenticular clouds.").setLanguage("en")
);
Response<RecognizeLinkedEntitiesResultCollection> response =
textAnalyticsClient.recognizeLinkedEntitiesBatchWithResponse(textDocumentInputs,
new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
// Response's status code
System.out.printf("Status code of request response: %d%n", response.getStatusCode());
RecognizeLinkedEntitiesResultCollection resultCollection = response.getValue();
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf(
"A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
resultCollection.forEach(recognizeLinkedEntitiesResult ->
recognizeLinkedEntitiesResult.getEntities().forEach(linkedEntity -> {
System.out.println("Linked Entities:");
System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(),
linkedEntity.getDataSource());
linkedEntity.getMatches().forEach(entityMatch -> System.out.printf(
"Matched entity: %s, confidence score: %.2f.%n",
entityMatch.getText(), entityMatch.getConfidenceScore()));
}));
Parameters:
Returns:
recognizePiiEntities
public PiiEntityCollection recognizePiiEntities(String document)
Returns a list of Personally Identifiable Information(PII) entities in the provided document. For a list of supported entity types, check: this For a list of enabled languages, check: this. This method will use the default language that is set using defaultLanguage(String language). If none is specified, service will use 'en' as the language.
Code Sample
Recognize the PII entities details in a document.
PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities("My SSN is 859-98-0987");
System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
for (PiiEntity entity : piiEntityCollection) {
System.out.printf(
"Recognized Personally Identifiable Information entity: %s, entity category: %s,"
+ " entity subcategory: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore());
}
Parameters:
Returns:
recognizePiiEntities
public PiiEntityCollection recognizePiiEntities(String document, String language)
Returns a list of Personally Identifiable Information(PII) entities in the provided document with provided language code. For a list of supported entity types, check: this For a list of enabled languages, check: this
Code Sample
Recognizes the PII entities details in a document with a provided language code.
PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities(
"My SSN is 859-98-0987", "en");
System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
piiEntityCollection.forEach(entity -> System.out.printf(
"Recognized Personally Identifiable Information entity: %s, entity category: %s,"
+ " entity subcategory: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
Parameters:
Returns:
recognizePiiEntities
public PiiEntityCollection recognizePiiEntities(String document, String language, RecognizePiiEntitiesOptions options)
Returns a list of Personally Identifiable Information(PII) entities in the provided document with provided language code. For a list of supported entity types, check: this For a list of enabled languages, check: this
Code Sample
Recognizes the PII entities details in a document with a provided language code and RecognizePiiEntitiesOptions.
PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities(
"My SSN is 859-98-0987", "en",
new RecognizePiiEntitiesOptions().setDomainFilter(PiiEntityDomain.PROTECTED_HEALTH_INFORMATION));
System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
piiEntityCollection.forEach(entity -> System.out.printf(
"Recognized Personally Identifiable Information entity: %s, entity category: %s,"
+ " entity subcategory: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
Parameters:
Returns:
recognizePiiEntitiesBatch
public RecognizePiiEntitiesResultCollection recognizePiiEntitiesBatch(Iterable
Returns a list of Personally Identifiable Information(PII) entities for the provided list of documents with provided language code and request options.
Code Sample
Recognizes the PII entities details in a list of documents with a provided language code and request options.
List<String> documents = Arrays.asList(
"My SSN is 859-98-0987",
"Visa card 4111 1111 1111 1111"
);
RecognizePiiEntitiesResultCollection resultCollection = textAnalyticsClient.recognizePiiEntitiesBatch(
documents, "en", new RecognizePiiEntitiesOptions().setIncludeStatistics(true));
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
resultCollection.forEach(recognizePiiEntitiesResult -> {
PiiEntityCollection piiEntityCollection = recognizePiiEntitiesResult.getEntities();
System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
piiEntityCollection.forEach(entity -> System.out.printf(
"Recognized Personally Identifiable Information entity: %s, entity category: %s,"
+ " entity subcategory: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
});
Parameters:
Returns:
recognizePiiEntitiesBatchWithResponse
public Response
Returns a list of Personally Identifiable Information(PII) entities for the provided list of TextDocumentInput with provided request options.
Code Sample
Recognizes the PII entities details with http response in a list of TextDocumentInput with provided request options.
List<TextDocumentInput> textDocumentInputs = Arrays.asList(
new TextDocumentInput("0", "My SSN is 859-98-0987"),
new TextDocumentInput("1", "Visa card 4111 1111 1111 1111")
);
Response<RecognizePiiEntitiesResultCollection> response =
textAnalyticsClient.recognizePiiEntitiesBatchWithResponse(textDocumentInputs,
new RecognizePiiEntitiesOptions().setIncludeStatistics(true), Context.NONE);
RecognizePiiEntitiesResultCollection resultCollection = response.getValue();
// Batch statistics
TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
resultCollection.forEach(recognizePiiEntitiesResult -> {
PiiEntityCollection piiEntityCollection = recognizePiiEntitiesResult.getEntities();
System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
piiEntityCollection.forEach(entity -> System.out.printf(
"Recognized Personally Identifiable Information entity: %s, entity category: %s,"
+ " entity subcategory: %s, confidence score: %f.%n",
entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
});
Parameters:
Returns:
Applies to
Azure SDK for Java
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