Document expansion for speech retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Language model information retrieval with document expansion
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A comparison of statistical significance tests for information retrieval evaluation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Advances in Multilingual and Multimodal Information Retrieval
Query Expansion Using External Evidence
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Multi-platform image search using tag enrichment
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
An LDA-smoothed relevance model for document expansion: a case study for spoken document retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Successful information retrieval requires effective matching between the user's search request and the contents of relevant documents. Often the request entered by a user may not use the same topic relevant terms as the authors' of these documents. One potential approach to address problems of query-document term mismatch is document expansion to include additional topically relevant indexing terms in a document which may encourage its retrieval when relevant to queries which do not match its original contents well. We propose and evaluate a new document expansion method using external resources. While results of previous research have been inconclusive in determining the impact of document expansion on retrieval effectiveness, our method is shown to work effectively for text-based image retrieval of short image annotation documents. Our approach uses the Okapi query expansion algorithm as a method for document expansion. We further show improved performance can be achieved by using a "document reduction" approach to include only the significant terms in a document in the expansion process. Our experiments on the WikipediaMM task at ImageCLEF 2008 show an increase of 16.5% in mean average precision (MAP) compared to a variation of Okapi BM25 retrieval model. To compare document expansion with query expansion, we also test query expansion from an external resource which leads an improvement by 9.84% in MAP over our baseline. Our conclusion is that the document expansion with document reduction and in combination with query expansion produces the overall best retrieval results for short-length document retrieval. For this image retrieval task, we also conclude that query expansion from external resources does not outperform the document expansion method.