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Vector Space Models Improve Semantic Document Retrieval
6.6
Relevance Score
In this explanatory piece, the author outlines how vector space models and contextual embeddings enable semantic information retrieval, citing TF-IDF, cosine similarity, BERT, RankBrain, and MUM. It explains core concepts such as representing documents as vectors, cosine-based similarity, document-length normalization, and transformers' contextual embeddings. The article emphasizes practical implications for improving relevance and ranking in search systems.
