MRF based approach for sentence retrieval

Authors:
Keke Cai;Chun Chen;Kangmiao Liu;Jiajun Bu;Peng Huang
Affiliations:
Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China
Venue:
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2007

Citing 1
Cited 2

A Markov random field model for term dependencies

Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

An improved markov random field model for supporting verbose queries

Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Syntactic Query Models for Restatement Retrieval

SPIRE '09 Proceedings of the 16th International Symposium on String Processing and Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

This poster focuses on the study of term context dependence in the application of sentence retrieval. Based on Markov Random Field (MRF), three forms of dependence among query terms are considered. Under different assumptions of term dependence relationship, three feature functions are defined, with the purpose to utilize association features between query terms in sentence to evaluate the relevance of sentence. Experimental results have proven the efficiency of the proposed retrieval models in improving the performance of sentence retrieval.