Roi Blanco
ABSTRACT
We present a way of estimating term weights for Information Retrieval (IR), using term co-occurrence as a measure of dependency between terms.We use the random walk graph-based ranking algorithm on a graph that encodes terms and co-occurrence dependencies in text, from which we derive term weights that represent a quantification of how a term contributes to its context. Evaluation on two TREC collections and 350 topics shows that the random walk-based term weights perform at least comparably to the traditional tf-idf term weighting, while they outperform it when the distance between co-occurring terms is between 6 and 30 terms.
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Digital Library
- S. Hassan and C. Banea. Random-Walk Term Weighting for Improved Text Classification. In Proceedings of TextGraphs: 2nd Workshop on Graph Based Methods for Natural Language Processing. ACL, 53--60, 2006. Google ScholarDigital Library
- R. Mihalcea and P.Tarau. TextRank: Bringing Order into Texts. In Proceedings of Empirical Methods in Natural Language Processing. ACL, 404--411, 2006.Google Scholar
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Index Terms
- Random walk term weighting for information retrieval
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