TY - CHAP

T1 - Random walk term weighting for information retrieval

AU - Blanco, R.

AU - Lioma, Christina

PY - 2007/1/1

Y1 - 2007/1/1

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.

UR - http://www.scopus.com/inward/record.url?scp=36448963046&partnerID=8YFLogxK

U2 - 10.1145/1277741.1277930

DO - 10.1145/1277741.1277930

M3 - Book chapter

AN - SCOPUS:36448963046

SN - 9781595935977

SP - 829

EP - 830

BT - Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07

ER -