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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