Abstract
Word sense representation is important in the tasks of information retrieval (IR). Existing lexical databases, e.g., WordNet, and automated word sense representing approaches often use only one view to represent a word, and may not work well in the tasks which are sensitive to the contexts, e.g., query rewriting. In this paper, we propose a new framework to represent a word sense simultaneously in two views, explanation view and context view. We further propose an novel method to automatically learn such representations from large scale of query logs. Experimental results show that our new sense representations can better handle word substitutions in a query rewriting task.
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Acknowledgement
We would like to thank Ben Xu, Wensong He, Shuaixiang Dai, Xiaozhao Zhao, Qiannan Lv, and the anonymous reviewers for their helpful feedback. This work is supported by National High Technology R&D Program of China (Grant No. 2015AA015403, 2014AA015102) and Natural Science Foundation of China (Grant No. 61202233, 61272344, 61370055). For any correspondence, please contact Liwei Chen.
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Chen, L., Feng, Y., Zhao, D. (2016). TDSS: A New Word Sense Representation Framework for Information Retrieval. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_6
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DOI: https://doi.org/10.1007/978-3-319-50496-4_6
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