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Information Retrieval via Truncated Hilbert-Space Expansions

2009/10/10 by Patricio Galeas, Galeas, Patricio, Ralph Kretschmer +3
Computer Science · #FOS: Computer and information sciences #H.3.3 #Information Retrieval (cs.IR) #cs.IR

paper · pdf · doi:10.48550/arxiv.0910.1938

12 pages, submitted to proceedings of ECIR-2010

arxiv created 2009/10/10 · arxiv updated 2009/12/01

Abstract

In addition to the frequency of terms in a document collection, the distribution of terms plays an important role in determining the relevance of documents. In this paper, a new approach for representing term positions in documents is presented. The approach allows an efficient evaluation of term-positional information at query evaluation time. Three applications are investigated: a function-based ranking optimization representing a user-defined document region, a query expansion technique based on overlapping the term distributions in the top-ranked documents, and cluster analysis of terms in documents. Experimental results demonstrate the effectiveness of the proposed approach.

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