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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