and average them evaluates performance at all recall

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Unformatted text preview: Google was using over 200 such features. Introduc)on to Informa)on Retrieval How to combine features to assign a relevance score to a document? §༊  Given lots of relevant features… §༊  You can con*nue to hand- engineer retrieval scores §༊  Or, you can build a classifier to learn weights for the features §༊  Requires: labeled training data §༊  This is the “learning to rank” approach, which has become a hot area in recent years §༊  I only provide an elementary introduc*on here Introduc)on to Informa)on Retrieval Sec. 15.4.1 Simple example: Using classifica*on for ad hoc IR §༊  Collect a training corpus of (q, d, r) triples §༊  Relevance r is here binary (but may be mul*class, with 3–7 values) §༊  Document is represented by a feature vector §༊  x = (α, ω) α is cosine similarity, ω is minimum query window size §༊  ω is the the shortest text span that includes all query words §༊  Query term proximity is a very important...
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