No abstract

No abstract

Search engines process queries conjunctively to restrict the size of the answer set. Further, it is not rare to observe a mismatch between the vocabulary used in the text of Web pages and the terms used to compose the Web queries. The combination of these two features might lead to irrelevant query results, particularly in the case of more specific queries composed of three or more terms. To deal with this problem we propose a new technique for automatically structuring Web queries as a set of smaller subqueries. To select representative subqueries we use information on their distributions in the document collection. This can be adequately modeled using the concept of maximal termsets derived from the formalism of association rules theory. Experimentation shows that our technique leads to improved results. For the TREC-8 test collection, for instance, our technique led to gains in average precision of roughly 28% with regard to a BM25 ranking formula.

No abstract

The objective of this paper is to present a new technique for computing term weights for index terms, which leads to a new ranking mechanism, referred to as set-based model. The components in our model are no longer terms, but termsets. The novelty is that we compute term weights using a data mining technique called association rules, which is time efficient and yet yields nice improvements in retrieval effectiveness. The set-based model function for computing the similarity between a document and a query considers the termset frequency in the document and its scarcity in the document collection. Experimental results show that our model improves the average precision of the answer set for all three collections evaluated. For the TReC-3 collection, our set-based model led to a gain, relative to the standard vector space model, of 37% in average precision curves and of 57% in average precision for the top 10 documents. Like the vector space model, the set-based model has time complexity that is linear in the number of documents in the collection.