Abstract
Using low dimensional vector space to represent words has been very effective in many NLP tasks. However, it doesn’t work well when faced with the problem of rare and unseen words. In this paper, we propose to leverage the knowledge in semantic dictionary in combination with some morphological information to build an enhanced vector space. We get an improvement of 2.3% over the state-of-the-art Heidel Time system in temporal expression recognition, and obtain a large gain in other name entity recognition (NER) tasks. The semantic dictionary Hownet alone also shows promising results in computing lexical similarity.
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Acknowledgement
This work is supported by National Natural Science Foundation of China (61371129), National Key Basic Research Program of China (2014CB340504), Key Program of Social Science foundation of China (12&ZD227), and the Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research (ICDD201402).
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Li, W., Wu, Y., Lv, X. (2016). Improving Word Vector with Prior Knowledge in Semantic Dictionary. 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_38
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DOI: https://doi.org/10.1007/978-3-319-50496-4_38
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