Exploration of term dependence in sentence retrieval

Authors:
Keke Cai;Jiajun Bu;Chun Chen;Kangmiao Liu
Affiliations:
Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China
Venue:
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Year:
2007

Citing 6
Cited 2

Automatic structuring and retrieval of large text files

Communications of the ACM
Syntactic features in question answering

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Using top-ranking sentences to facilitate effective information access: Book Reviews

Journal of the American Society for Information Science and Technology
Novelty detection based on sentence level patterns

Proceedings of the 14th ACM international conference on Information and knowledge management
Bayesian query-focused summarization

ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A translation model for sentence retrieval

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing

An effective approach for searching closest sentence translations from the web

DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Terminological paraphrase extraction from scientific literature based on predicate argument tuples

Journal of Information Science

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper focuses on the exploration of term dependence in the application of sentence retrieval. The adjacent terms appearing in query are assumed to be related with each other. These assumed dependences among query terms will be further validated for each sentence and sentences, which present strong syntactic relationship among query terms, are considered more relevant. Experimental results have fully demonstrated the promising of the proposed models in improving sentence retrieval effectiveness.