信息检索六tfidf.ppt

湖南大学计算机与通信学院 刘钰峰 回顾 1、中文分词 2、词典压缩 3、posting list压缩 4、tfidf Scoring documents How do we construct an index? What strategies can we use with limited main memory? Scoring We wish to return in order the documents most likely to be useful to the searcher How can we rank order the docs in the corpus with respect to a query? Assign a score – say in [0,1] for each doc on each query Begin with a perfect world – no spammers Nobody stuffing keywords into a doc to make it match queries More on “adversarial IR” under web search Linear zone combinations First generation of scoring methods: use a linear combination of Booleans: E.g., Score = 0.6*sorting in Title + 0.3*sorting in Abstract + 0.05*sorting in Body + 0.05*sorting in Boldface Each expression such as sorting in Title takes on a value in {0,1}. Then the overall score is in [0,1]. Exercise On the query bill OR rights suppose that we retrieve the following docs from the various zone indexes: General idea We are given a weight vector whose components sum up to 1. There is a weight for each zone/field. Given a Boolean query, we assign a score to each doc by adding up the weighted contributions of the zones/fields. Typically – users want to see the K highest-scoring docs. Index support for zone combinations In the simplest version we have a separate inverted index for each zone Variant: have a single index with a separate dictionary entry for each term and zone E.g., Zone combinations index The above scheme is still wasteful: each term is potentially replicated for each zone In a slightly better scheme, we encode the zone in the postings: At query time, accumulate contributions to the total score of a document from the various postings, e.g., Score accumulation As we walk the postings for the query bill OR rights, we accumulate scores for each doc in a linear merge as before. Note: we get both bill and rights in the Title field of doc 3, but score it no higher. Should we give more weight to more hits? Term-document