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Natural Language Processing in Action
book

Natural Language Processing in Action

by Cole Howard, Hobson Lane, Hannes Hapke
April 2019
Intermediate to advanced content levelIntermediate to advanced
544 pages
17h 29m
English
Manning Publications
Content preview from Natural Language Processing in Action

4 Finding meaning in word counts (semantic analysis)

This chapter covers

  • Analyzing semantics (meaning) to create topic vectors
  • Semantic search using the similarity between topic vectors
  • Scalable semantic analysis and semantic search for large corpora
  • Using semantic components (topics) as features in your NLP pipeline
  • Navigating high-dimensional vector spaces

You’ve learned quite a few natural language processing tricks. But now may be the first time you’ll be able to do a little bit of magic. This is the first time we talk about a machine being able to understand the “meaning” of words.

The TF-IDF vectors (term frequency–inverse document frequency vectors) from chapter 3 helped you estimate the importance of words in a chunk of text. You used ...

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