SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improved query difficulty prediction for the web
Proceedings of the 17th ACM conference on Information and knowledge management
An effective coherence measure to determine topical consistency in user-generated content
International Journal on Document Analysis and Recognition - Special Issue NOISY
Annotation of heterogeneous multimedia content using automatic speech recognition
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Visual concept-based selection of query expansions for spoken content retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Exploiting noisy visual concept detection to improve spoken content based video retrieval
Proceedings of the international conference on Multimedia
Explaining query modifications: an alternative interpretation of term addition and removal
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
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We propose a technique that predicts both if and how expansion should be applied to individual queries. The prediction is made on the basis of the topical consistency of the top results of the initial results lists returned by the unexpanded query and several query expansion alternatives. We use the coherence score, known to capture the tightness of topical clustering structure, and also propose two simplified coherence indicators. We test our technique in a spoken content retrieval task, with the intention of helping to control the effects of speech recognition errors. Experiments use 46 semantic-theme-based queries defined by VideoCLEF 2009 over the TRECVid 2007 and 2008 video data sets. Our indicators make the best choice roughly 50% of the time. However, since they predict the right query expansion in critical cases, overall MAP improves. The approach is computationally lightweight and requires no training data.