On term selection for query expansion
Journal of Documentation
An information-theoretic approach to automatic query expansion
ACM Transactions on Information Systems (TOIS)
Using WordNet and Lexical Operators to Improve Internet Searches
IEEE Internet Computing
Improving pseudo-relevance feedback in web information retrieval using web page segmentation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Mining dependency relations for query expansion in passage retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A review of ontology based query expansion
Information Processing and Management: an International Journal
Estimation and use of uncertainty in pseudo-relevance feedback
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Reducing the risk of query expansion via robust constrained optimization
Proceedings of the 18th ACM conference on Information and knowledge management
A unified relevance model for opinion retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
| Hi-index | 0.00 |
Pseudo-relevance feedback via query expansion has been widely studied from various perspectives in the past decades. Its effectiveness in improving retrieval effectiveness has been shown in many tasks. A variety of criteria were proposed to select additional terms for the original queries. However, most of the existing methods weight and select terms individually and do not consider the impact of term-to-term relationship. In this paper, we first examine the influence of combinations of terms through data analysis, which demonstrate the significant effect of term-to-term relationship on retrieval effectiveness. Then, to address this problem, we formalize the query expansion task as an integer linear programming (ILP) problem. The model combines the weights learned from a supervised method for individual terms, and integrates constraints to capture relations between terms. Finally, three standard TREC collections are used to evaluate the proposed method. Experimental results demonstrate that the proposed method can significantly improve the effectiveness of retrieval.