Lecture_12_01 - Vector Space Scoring Introduction to...

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Vector Space Scoring Introduction to Information Retrieval INF 141/ CS 121 Donald J. Patterson Content adapted from Hinrich Schütze http://www.informationretrieval.org
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Spamming indices Vector Space Scoring This was invented before spam Consider: Indexing a sensible passive document collection vs. Indexing an active document collection, where people, companies, bots are shaping documents to maximize scores Vector space scoring may not be as useful in this context.
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Interaction: vectors and phrases Vector Space Scoring Scoring phrases doesn’t naturally fit into the vector space world: How do we get beyond the “bag of words”? “dark roast” and “pot roast” There is no information on “dark roast” as a phrase in our indices. Biword index can treat some phrases as terms postings for phrases document wide statistics for phrases
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Interaction: vectors and phrases Vector Space Scoring Theoretical problem: Axes of our term space are now correlated There is a lot of shared information in “light roast” and “dark roast” rows of our index End-user problem: A user doesn’t know which phrases are indexed and can’t effectively discriminate results.
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Multiple queries for phrases and vectors Vector Space Scoring Query: “rising interest rates” Iterative refinement: Run the phrase query vector with 3 words as a term. If not enough results, run 2-phrase queries and fold into results: “rising interest” “interest rates” If still not enough results run query with three words as separate terms.
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