Introduction

Completed

In order for computer systems to interpret the subject of a text in a similar way humans do, they use natural language processing (NLP), an area within AI that deals with understanding written or spoken language, and responding in kind. Text analysis describes NLP processes that extract information from unstructured text.

Some common NLP text analysis use cases are:

1. Speech-to-text and text-to-speech conversion. For example, generate subtitles for videos.
2. Machine translation. For example, translate text from English to Japanese.
3. Text classification. For example, label an email as spam or not spam.
4. Entity extraction. For example, extract keywords or names from a document.
5. Question answering. For example, provide answers to questions like "What is the capital of France?"
6. Text summarization. For example, generate a short one-paragraph summary from a multi-page document.

Historically, NLP has been challenging as our language is complex and computers find it hard to understand text. In this module, you learn how developments in AI and specifically NLP have led to the models we use today.

Next, let's examine some general principles and common techniques used to perform text analysis and other NLP tasks.