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|Title:||A lazy learning model for entity linking using query-specific information||Authors:||Zhang, W.
|Issue Date:||2012||Citation:||Zhang, W.,Su, J.,Tan, C.L.,Cao, Y.,Yewlin, C. (2012). A lazy learning model for entity linking using query-specific information. 24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers : 3089-3104. ScholarBank@NUS Repository.||Abstract:||Entity linking disambiguates a mention of an entity in text to a Knowledge Base (KB). Most previous studies disambiguate a mention of a name (e.g."AZ") based on the distribution knowledge learned from labeled instances, which are related to other names (e.g."Hoffman","Chad Johnson", etc.). The gaps among the distributions of the instances related to different names hinder the further improvement of the previous approaches. This paper proposes a lazy learning model, which allows us to improve the learning process with the distribution information specific to the queried name (e.g."AZ"). To obtain this distribution information, we automatically label some relevant instances for the queried name leveraging its unambiguous synonyms. Besides, another advantage is that our approach still can benefit from the labeled data related to other names (e.g."Hoffman","Chad Johnson", etc.), because our model is trained on both the labeled data sets of queried and other names by mining their shared predictive structure. © 2012 The COLING.||Source Title:||24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers||URI:||http://scholarbank.nus.edu.sg/handle/10635/77961|
|Appears in Collections:||Staff Publications|
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