Chapter 5. AI for natural language

 

This chapter covers

  • Understanding the main challenges of natural language processing
  • Measuring opinions from text with sentiment analysis
  • Searching for textual content with natural queries
  • Building conversational interfaces (chatbots)
  • Case study: ML-assisted language translation

Making computers understand language has been a pipe dream of computer scientists since the 1950s. This is probably because most human knowledge and culture is encoded in written words and the use of language is one of the most powerful abilities that sets us apart from animals. The umbrella term for these techniques, which includes both understanding and producing language, is natural language processing ( NLP ).

5.1 The allure of natural language understanding

5.2 Breaking down NLP: Measuring complexity

5.3 Adding NLP capabilities to your organization

5.3.1 Sentiment analysis

5.3.2 From sentiment analysis to text classification

5.3.3 Scoping a NLP classification project

5.3.4 Document search

5.3.5 Natural conversation

5.3.6 Designing products that overcome technology limitations

5.4 Case study: Translated

5.4.1 Case questions

5.4.2 Case discussion

Summary