Information Retrieval and Web Search IR models: Vector Space Model Term Weighting Approaches Instructor: Rada Mihalcea

Graphic Representation Example: D 1 = 2T 1 + 3T 2 + 5T 3 D 2 = 3T 1 + 7T 2 + T 3 Q = 0T 1 + 0T 2 + 2T 3 T3T3 T1T1 T2T2 D 1 = 2T 1 + 3T 2 + 5T 3 D 2 = 3T 1 + 7T 2 + T 3 Q = 0T 1 + 0T 2 + 2T Is D 1 or D 2 more similar to Q? How to measure the degree of similarity? Distance? Angle? Projection?

Document Collection Representation A collection of n documents can be represented in the vector space model by a term-document matrix. An entry in the matrix corresponds to the “ weight ” of a term in the document; zero means the term has no significance in the document or it simply doesn’t exist in the document. T 1 T 2 …. T t D 1 w 11 w 21 … w t1 D 2 w 12 w 22 … w t2 : : : : D n w 1n w 2n … w tn

Term Frequency tf The term frequency tf t,d of term t in document d is defined as the number of times that t occurs in d. More frequent terms in a document are more important, i.e., more indicative of the topic. May want to normalize term frequency (tf): tf t,d = f t,d / max{f t,d }

Document Frequency Rare terms are more informative than frequent terms –Recall stop words Consider a term in the query that is rare in the collection (e.g., arachnocentric) A document containing this term is very likely to be relevant to the query arachnocentric → We want a high weight for rare terms like arachnocentric. Sec

Idf Weight df t is the document frequency of t: the number of documents that contain t –df t is an inverse measure of the informativeness of t –df t  N We define the idf (inverse document frequency) of t by –We use log (N/df t ) instead of N/df t to “dampen” the effect of idf. Sec

tf-idf Weighting The tf-idf weight of a term is the product of its tf weight and its idf weight. Best known weighting scheme in information retrieval –Theoretically proven to work well (Papineni, NAACL 2001) –Note: the “-” in tf-idf is a hyphen, not a minus sign! –Alternative names: tf.idf, tf x idf Increases with the number of occurrences within a document Increases with the rarity of the term in the collection Sec

Computing tf-idf: An Example Given a document containing terms with given frequencies: A(3), B(2), C(1) Assume collection contains 10,000 documents and document frequencies of these terms are: A(50), B(1300), C(250) Then: A: tf = 3/3; idf = log(10000/50) = 5.3; tf-idf = 5.3 B: tf = 2/3; idf = log(10000/1300) = 2.0; tf-idf = 1.3 C: tf = 1/3; idf = log(10000/250) = 3.7; tf-idf = 1.2

Salton & Buckley Experiments with term weighting approaches Six collections, 1,800 term weighting approaches –287 combinations found to be distinct Comparative evaluations using: –Ranking: the lower the better –Best weighting scheme has a rank of 1 Average search precision: –Average of precisions for recall points of 0.25, 0.50, 0.75 Average across queries: –Macro-average: average of the average search precisions

IR Test Collections CACM (articles from 'Communications of the ACM journal) CISI (articles about information sciences) CRAN (abstracts from aeronautics articles) INSPEC (articles in computer engineering) MED (medical articles) NPL (articles about electrical engineering)

IR Collections

Term Weighting Components

Sample Weighting Schemes

Performance Results

Lessons Learned Term weighting DOES matter Query vector: –Term frequency Use n for short queries Use t for longer queries that require better discrimination among terms –Document frequency Use f –Do not do normalization with query length

Lessons Learned Document vectors: –Term-frequency: For technical vocabulary (e.g., CRAN) use n For more varied vocabulary, use t For short document vectors, use b –Document frequency: Inverse document-frequency f is similar to probabilistic term weight p: typically use f For dynamic collections with many changes in the document collection makeup, use x –Normalization: Typically use c (in particular when there is high deviation in vector length) For short documents with homogenous length, use x