# 12.8 Machine Learning and Deep Learning Natural Language Applications¶

- Answering natural language questions—For example, our publisher Pearson Education, has a partnership with IBM Watson that uses Watson as a virtual tutor. Students ask Watson natural language questions and get answers.
- Summarizing documents—analyzing documents and producing short summaries (abstracts) that can, for example, be included with search results and can help you decide what to read.
- Speech synthesis (speech-to-text), speech recognition (text-to-speech), inter-language text-to-text translation.
- Collaborative filtering—used to implement recommender systems (“if you liked this movie, you might also like…”).
- Text classification—e.g., classifying news articles by categories, such as world news, national news, local news, sports, business, entertainment, etc.
- Topic modeling—finding the topics discussed in documents.
- Sarcasm detection—often used with sentiment analysis.
- Text simplification—making text more concise and easier to read.
- Speech to sign language and vice versa—to enable a conversation with a hearing-impaired person.
- Lip reader technology—for people who can’t speak, convert lip movement to text or speech to enable conversation.
- Closed captioning—automatically adding text captions to video.

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