Query expansion with long-span collocates
The paper presents two novel approaches to query expansion with long-span collocates-words, significantly co-occurring in topic-size windows with query terms. In the first approach-global collocation analysis-collocates of query terms are extracted from the entire collection, in the second-local collocation analysis-from a subset of retrieved documents. The significance of association between collocates was estimated using modified Mutual Information and Z score. The techniques were tested using the Okapi IR system. The effect of different parameters on performance was evaluated: window size, number of expansion terms, measures of collocation significance and types of expansion terms. We present performance results of these techniques and provide comparison with related approaches.
long-span word collocation automatic query expansion relevance feedback probabilistic information retrieval