Random walk term weighting for information retrieval

TY - GEN

T1 - Random walk term weighting for information retrieval

AU - Lioma, Christina

AU - Blanco, Roi

N1 - Copyright is held by the author/owner(s). SIGIR’07, July 23–27, 2007, Amsterdam, The Netherlands. ACM 978-1-59593-597-7/07/0007.

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

M3 - Article in proceedings

SP - 829

EP - 830

BT - SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

PB - Association for Computing Machinery

ER -