Exploring semantic constraints for document retrieval

Authors:
Hua Cheng;Yan Qu;Jesse Montgomery;David A. Evans
Affiliations:
Clairvoyance Corporation, Pittsburgh, PA;Clairvoyance Corporation, Pittsburgh, PA;Clairvoyance Corporation, Pittsburgh, PA;Clairvoyance Corporation, Pittsburgh, PA
Venue:
CLIIR '06 Proceedings of the Workshop on How Can Computational Linguistics Improve Information Retrieval?
Year:
2006

Citing 4
Cited 0

CLARIT-TREC experiments

TREC-2 Proceedings of the second conference on Text retrieval conference
Analyses of multiple evidence combination

Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
The String-to-String Correction Problem

Journal of the ACM (JACM)
Towards effective strategies for monolingual and bilingual information retrieval: Lessons learned from NTCIR-4

ACM Transactions on Asian Language Information Processing (TALIP)

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we explore the use of structured content as semantic constraints for enhancing the performance of traditional term-based document retrieval in special domains. First, we describe a method for automatic extraction of semantic content in the form of attribute-value (AV) pairs from natural language texts based on domain models constructed from a semi-structured web resource. Then, we explore the effect of combining a state-of-the-art term-based IR system and a simple constraint-based search system that uses the extracted AV pairs. Our evaluation results have shown that such combination produces some improvement in IR performance over the term-based IR system on our test collection.