Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yom-Tov, E., Fine, S., Carmel, D., Darlow, A.: Learning to estimate query difficulty. In: SIGIR 2005, pp. 512–519 (2005)
Hauff, C., Murdock, V., Yates, R.B.: Improved query difficulty prediction for the web. In: CIKM 2008, pp. 439–448 (2008)
Byrne, W., et al.: Automatic recognition of spontaneous speech for access to multilingual oral history archives. IEEE Trans. SAP 12(4), 420–435 (2004)
Huijbregts, M., Ordelman, R., de Jong, F.: Annotation of heterogeneous multimedia content using automatic speech recognition. In: Falcidieno, B., Spagnuolo, M., Avrithis, Y., Kompatsiaris, I., Buitelaar, P. (eds.) SAMT 2007. LNCS, vol. 4816, pp. 78–90. Springer, Heidelberg (2007)
Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: SIGIR 2002, pp. 299–306 (2002)
Olsson, J.S., Oard, D.W.: Combining Speech Retrieval Results with Generalized Additive Models. In: ACL 2008: HLT, pp. 461–469 (2008)
He, J., Weerkamp, W., Larson, M., de Rijke, M.: An effective coherence measure to determine topical consistency in user-generated content. International Journal on Document Analysis and Recognition 12(3), 185–203 (2009)
Rudinac, S., Larson, M., Hanjalic, A.: Exploiting visual reranking to improve pseudo-relev-ance feedback for spoken-content-based video retrieval. In: WIAMIS 2009, pp. 17–20 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rudinac, S., Larson, M., Hanjalic, A. (2010). Exploiting Result Consistency to Select Query Expansions for Spoken Content Retrieval. In: Gurrin, C., et al. Advances in Information Retrieval. ECIR 2010. Lecture Notes in Computer Science, vol 5993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12275-0_67
Download citation
DOI: https://doi.org/10.1007/978-3-642-12275-0_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12274-3
Online ISBN: 978-3-642-12275-0
eBook Packages: Computer ScienceComputer Science (R0)