Improving English and Chinese Ad-Hoc Retrieval: A Tipster Text Phase 3 Project Report
Information Retrieval
Retrieval and feedback models for blog feed search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Investigating external corpus and clickthrough statistics for query expansion in the legal domain
Proceedings of the 17th ACM conference on Information and knowledge management
Entity-based query reformulation using wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Query Expansion Using External Evidence
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A generative blog post retrieval model that uses query expansion based on external collections
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Overview of the WikipediaMM task at ImageCLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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In text-based image retrieval, the Incomplete Annotation Problem (IAP) can greatly degrade retrieval effectiveness. A standard method used to address this problem is pseudo relevance feedback (PRF) which updates user queries by adding feedback terms selected automatically from top ranked documents in a prior retrieval run. PRF assumes that the target collection provides enough feedback information to select effective expansion terms. This is often not the case in image retrieval since images often only have short metadata annotations leading to the IAP. Our work proposes the use of an external knowledge resource (Wikipedia) in the process of refining user queries. In our method, Wikipedia documents strongly related to the terms in user query ("definition documents") are first identified by title matching between the query and titles of Wikipedia articles. These definition documents are used as indicators to re-weight the feedback documents from an initial search run on a Wikipedia abstract collection using the Jaccard coefficient. The new weights of the feedback documents are combined with the scores rated by different indicators. Query-expansion terms are then selected based on these new weights for the feedback documents. Our method is evaluated on the ImageCLEF WikipediaMM image retrieval task using text-based retrieval on the document metadata fields. The results show significant improvement compared to standard PRF methods.