Query Operations: Automatic Global Analysis

Motivation Methods of local analysis extract information from local set of documents retrieved to expand the query An alternative is to expand the query using information from the whole set of documents Until the beginning of the 1990s these techniques failed to yield consistent improvements in retrieval performance Now, with moderns variants, sometimes based on a thesaurus, this perception has changed

Automatic Global Analysis There are two modern variants based on a thesaurus-like structure built using all documents in collection –Query Expansion based on a Similarity Thesaurus –Query Expansion based on a Statistical Thesaurus

Similarity Thesaurus The similarity thesaurus is based on term-to-term relationships rather than on a matrix of co- occurrence. –These relationships are not derived directly from co- occurrence of terms inside documents. –They are obtained by considering that the terms are concepts in a concept space. –In this concept space, each term is indexed by the documents in which it appears. Terms assume the original role of documents while documents are interpreted as indexing elements

Similarity Thesaurus vs. Vector Model The frequency factor: –In vector model f (i,j) = freq ( term k i in doc d j ) / freq ( most common term in d j ) –In similarity thesaurus f (i,j) = freq ( term k i in doc d j ) / freq ( doc where term k i appears most) The inverse frequency factor: –In vector model Idf (i) = log (# of docs in collection / # of docs with term k i ) –In similarity thesaurus Itf (j) = log (# of terms in collection / # of terms in doc d j )

Similarity Thesaurus Definitions: –t: number of terms in the collection –N: number of documents in the collection –f i,j : frequency of occurrence of the term k i in the document d j –t j : vocabulary of document d j –itf j : inverse term frequency for document d j Inverse term frequency for document d j For each term k i where w i,j is a weight associated between the term and the documents.

Similarity Thesaurus The relationship between two terms k u and k v is computed as a correlation factor c u,v given by The global similarity thesaurus is built through the computation of correlation factor C u,v for each pair of indexing terms [k u,k v ] in the collection The computation is expensive but only has to be computed once and can be updated incrementally

Query Expansion based on a Similarity Thesaurus Query expansion is done in three steps as follows:  Represent the query in the concept space used for representation of the index terms 2 Based on the global similarity thesaurus, compute a similarity sim(q,k v ) between each term k v correlated to the query terms and the whole query q. 3 Expand the query with the top r ranked terms according to sim(q,k v )

Statistical Thesaurus Global thesaurus is composed of classes which group correlated terms in the context of the whole collection –Such correlated terms can then be used to expand the original user query –These terms must be low frequency terms –However, it is difficult to cluster low frequency terms –To circumvent this problem, we cluster documents into classes instead and use the low frequency terms in these documents to define our thesaurus classes. –This algorithm must produce small and tight clusters.

Complete Link Algorithm Document clustering algorithm –Place each document in a distinct cluster. –Compute the similarity between all pairs of clusters. –Determine the pair of clusters [C u,C v ] with the highest inter-cluster similarity. –Merge the clusters C u and C v –Verify a stop criterion. If this criterion is not met then go back to step 2. –Return a hierarchy of clusters. Similarity between two clusters is defined as the minimum of similarities between all pair of inter-cluster documents –Use of minimum ensures small, focussed clusters

Generating the Thesaurus Given the document cluster hierarchy for the whole collection –Which clusters become classes? –Which terms represent classes? Answers based on three parameters specified by operator based on characteristics of the collection. –TC: Threshold class –NDC: Number of documents in class –MIDF: Minimum inverse document frequency

Selecting Thesaurus Classes TC is the minimum similarity between two subclusters for the parent to be considered a class. –A high value makes classes smaller and more focussed. NDC is an upper limit on the size of clusters. –A low value of NDC restricts the selection to smaller, more focussed clusters

Picking Terms for Each Class Consider the set of documents in each class selected above Only the lower frequency terms are used for the thesaurus classes The parameter MIDF defines the minimum inverse document frequency for any term which is selected to participate in a thesaurus class

Query Expansion with Statisitcal Thesaurus For each thesaurus class C: –Compute an average term weight wt c –Compute the thesaurus class weight w c –Where |C| is the number of terms in the thesaurus class and w i,C is a precomputed weight associated with the term-class pair [K i, C]

Initializing TC, NDC, and MIDF TC depends on the collection –Inspection of the cluster hierarchy is almost always necessary for assisting with the setting of TC. –A high value of TC might yield classes with too few terms –A low value of TC yields too few classes NDC is easier to set once TC is set MIDF can be difficult to set

Conclusions Automatically generated thesaurus is an efficient method to expand queries Thesaurus generation is expensive but it is executed only once Query expansion based on similarity thesaurus uses term frequencies to expand the query Query expansion based on statistical thesaurus needs well defined parameters