Query expansion with the minimum user feedback by transductive learning

Authors:
Masayuki Okabe;Kyoji Umemura;Seiji Yamada
Affiliations:
Toyohashi University of Technology, Aichi, Japan;Toyohashi University of Technology, Aichi, Japan;National Institute of Informatics, Tokyo, Japan
Venue:
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Year:
2005

Citing 15
Cited 2

On term selection for query expansion

Journal of Documentation
Incremental relevance feedback

SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory

The nature of statistical learning theory
Incremental relevance feedback for information filtering

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance feedback with a small number of relevance judgements: incremental relevance feedback vs. document clustering

SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Applying summarization techniques for term selection in relevance feedback

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting query modifications from nonlinear SVMs

Proceedings of the 11th international conference on World Wide Web
Transductive Inference for Text Classification using Support Vector Machines

ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Improving pseudo-relevance feedback in web information retrieval using web page segmentation

WWW '03 Proceedings of the 12th international conference on World Wide Web
Re-examining the potential effectiveness of interactive query expansion

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
SIGIR 2003 workshop report: implicit measures of user interests and preferences

ACM SIGIR Forum
Semi-supervised learning using randomized mincuts

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Manifold-ranking based image retrieval

Proceedings of the 12th annual ACM international conference on Multimedia
The text retrieval conferences (TRECS)

TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Keyword spices: a new method for building domain-specific web search engines

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2

Semisupervised Query Expansion with Minimal Feedback

IEEE Transactions on Knowledge and Data Engineering
Psychiatric document retrieval using a discourse-aware model

Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Query expansion techniques generally select new query terms from a set of top ranked documents. Although a user's manual judgment of those documents would much help to select good expansion terms, it is difficult to get enough feedback from users in practical situations. In this paper we propose a query expansion technique which performs well even if a user notifies just a relevant document and a non-relevant document. In order to tackle this specific condition, we introduce two refinements to a well-known query expansion technique. One is application of a transductive learning technique in order to increase relevant documents. The other is a modified parameter estimation method which laps the predictions by multiple learning trials and try to differentiate the importance of candidate terms for expansion in relevant documents. Experimental results show that our technique outperforms some traditional query expansion methods in several evaluation measures.