On term selection for query expansion
Journal of Documentation
The smart document retrieval project
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
On relevance weights with little relevance information
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
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
Semi-supervised learning using randomized mincuts
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Mutual relevance feedback for multimodal query formulation in video retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Improving the estimation of relevance models using large external corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A study on the effects of personalization and task information on implicit feedback performance
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Query expansion with the minimum user feedback by transductive learning
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
Information Systems
Information Systems
Mining linguistic cues for query expansion: applications to drug interaction search
Proceedings of the 18th ACM conference on Information and knowledge management
Ontology based query expansion in vertical search engine
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Exploiting the query expansion through knowledgebases for images
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part II
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Query expansion is an information retrieval technique in which new query terms are selected to improve search performance. Although useful terms can be extracted from documents whose relevance is already known, it is difficult to get enough of such feedback from a user in actual use. We propose a query expansion method that performs well even if a user makes practically minimum effort, that is, chooses only a single relevant document. To improve searches in these conditions, we made two refinements to a well-known query expansion method. One uses transductive learning to obtain pseudo relevant documents, thereby increasing the total number of source documents from which expansion terms can be extracted. The other is a modified parameter estimation method that aggregates the predictions of multiple learning trials to sort candidate terms for expansion by importance. Experimental results show that our method outperforms traditional methods, and is comparable to a state of the art method.