Chapter 5. Ranking search results with word embeddings
This chapter covers
- Statistical and probabilistic retrieval models
- Working with the ranking algorithm in Lucene
- Neural information retrieval models
- Using averaged word embeddings to rank search results
Since chapter 2, we’ve been building components based on neural networks that can improve a search engine. These components aim to help the search engine better capture user intent by expanding synonyms, generating alternative representations of a query, and giving smarter suggestions while the user is typing a query. As these approaches show, a query can be expanded, adapted, and transformed before matching with the terms stored in the inverted indexes is performed. Then, as mentioned ...
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