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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/62179

Title: A Late Fusion Approach to Cross-Lingual Document Re-ranking
Author: ZHOU, DONG
WADE, VINCENT PATRICK
LAWLESS, SEAMUS
Author's Homepage: http://people.tcd.ie/selawles
http://people.tcd.ie/zhoud
http://people.tcd.ie/vwade
Keywords: Information Systems
Cross-lingual Information Retrieval
Issue Date: 2010
Publisher: ACM
Citation: Dong Zhou, Seamus Lawless, Jinming Min and Vincent Wade, A Late Fusion Approach to Cross-Lingual Document Re-ranking, In the Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, Toronto, Canada, October 26-30, 2010, ACM, 2010, 1433 - 1436
Abstract: The field of information retrieval still strives to develop models which allow semantic information to be integrated in the ranking process to improve performance in comparison to standard bag- of-words based models. Cross-lingual information retrieval is an example of where such a model is required, as content or concepts often need to be matched across languages. To overcome this problem, a conceptual model has been adopted in ranking an entire corpus which normally exploits latent/implicit features of the text. One of the drawbacks of this model is that the computational cost is significant and often intractable in modern test collections. Therefore, approaches utilizing concept- based models for re-ranking initial retrieval results have attracted a considerable amount of study, in particular the latent concept model. However, fitting such a model to a smaller collection is less meaningful than fitting it into the whole corpus. This paper proposes a late fusion method which incorporates scores generated by using external knowledge to enhance the space produced by the latent concept method. This method is further demonstrated to be suitable for multilingual re-ranking purposes. To illustrate the effectiveness of the proposed method, experiments were conducted over test collections across three languages. The results demonstrate that the method can comfortably achieve improvements in retrieval performance over several re-ranking methods.
Description: PUBLISHED
URI: http://hdl.handle.net/2262/62179
Related links: http://www.scss.tcd.ie/seamus.lawless/papers/CIKM2010.pdf
http://dl.acm.org/citation.cfm?id=1871640
Appears in Collections:Computer Science (Scholarly Publications)

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