Document expansion for speech retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Proceedings of the thirteenth ACM international conference on Information and knowledge management
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Document expansion for image retrieval
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
New metrics for meaningful evaluation of informally structured speech retrieval
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Improving retrieval of short texts through document expansion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Document expansion (DE) in information retrieval (IR) involves modifying each document in the collection by introducing additional terms into the document. It is particularly useful to improve retrieval of short and noisy documents where the additional terms can improve the description of the document content. Existing approaches to DE assume that documents to be expanded are from a single topic. In the case of multi-topic documents this can lead to a topic bias in terms selected for DE and hence may result in poor retrieval quality due to the lack of coverage of the original document topics in the expanded document. This paper proposes a new DE technique providing a more uniform selection and weighting of DE terms from all constituent topics. We show that our proposed method significantly outperforms the most recently reported relevance model based DE method on a spoken document retrieval task for both manual and automatic speech recognition transcripts.