Azure Language Understanding Sentiment/Sentence similarity

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Azure Language Understanding Sentiment/Sentence similarity

Bashitha Jayathunga 21

Using Azure LUIS (Language Understanding), how can two strings be compared and their similarity score be obtained to see if they mean the same or not when phrased differently? Like for example, "I got 10 dollars in my pocket" and "I have only 10 dollars remaining in my pocket" being the two strings. Here, how can the two strings be compared by LUIS to obtain a similarity score (to what extent their meanings match/mean the same)? Also, I'm doing a UWP project so preferably in C#.

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Ramr-msft 17,826 Reputation points

2021-06-21T15:20:34.457+00:00

@BashithaJayathunga-8818 Thanks for the question. Can you please add more details about the usecase that you are trying. Cognitive services Text Analytics API has the capability for sentiment analysis. Please follow the document link for the same. We have forwarded to the product team to check for the LUIS.
Bashitha Jayathunga 21 Reputation points

2021-06-21T16:34:29.427+00:00

My app is C# .Net Core. What it does is making question papers. Here, the setter of the question can specify which criteria should be present in an ideal answer and should allocate marks for each criteria. When a student answers, the answer should be read and compared to the criteria by NLP. Marks would be added for each criterion present in the answer. I use NPL to check if the sections of the answer phrased differently from the criteria mean the same by similarity analysis with a similarity score between two strings (sentiments). I believe I studied all of your available documentation about this is Azure LUIS and Text Analysis but did not find anything related. Sentiment analysis in the Text Analysis documentation doesn't provide a solution to finding sentence similarity. Could you please show me the method by which Similarity could be obtained between two sentiments using Sentiment Analysis if it is possible with Azure Text Analysis?

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Ramr-msft 17,826 Reputation points

2021-06-21T15:20:34.457+00:00

@BashithaJayathunga-8818 Thanks for the question. Can you please add more details about the usecase that you are trying. Cognitive services Text Analytics API has the capability for sentiment analysis. Please follow the document link for the same. We have forwarded to the product team to check for the LUIS.
Bashitha Jayathunga 21 Reputation points

2021-06-21T16:34:29.427+00:00

My app is C# .Net Core. What it does is making question papers. Here, the setter of the question can specify which criteria should be present in an ideal answer and should allocate marks for each criteria. When a student answers, the answer should be read and compared to the criteria by NLP. Marks would be added for each criterion present in the answer. I use NPL to check if the sections of the answer phrased differently from the criteria mean the same by similarity analysis with a similarity score between two strings (sentiments). I believe I studied all of your available documentation about this is Azure LUIS and Text Analysis but did not find anything related. Sentiment analysis in the Text Analysis documentation doesn't provide a solution to finding sentence similarity. Could you please show me the method by which Similarity could be obtained between two sentiments using Sentiment Analysis if it is possible with Azure Text Analysis?

Answer 1

@BashithaJayathunga-8818 Thanks for the details. We don't have a pre-built offering for Sentence similarity to analyze that in TA/LUIS. The Microsoft NLP Best practices has an example using Horovod to train a genism sentence similarity model we recommend looking at it for reference.

https://github.com/microsoft/nlp/blob/46c0658b79208763e97ae3171e9728560fe37171/examples/sentence_similarity/gensen_train.py

You can also raise a user voice request here so the community can vote and provide their feedback, the product team then checks this feedback and implements the feature in future releases.

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Azure Language Understanding Sentiment/Sentence similarity

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