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.\u201cAZ\u201d) based on the distribution knowledge learned from labeled instances, which are related to other names (e.g.\u201cHoffman\u201d,\u201cChad Johnson\u201d, 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.\u201cAZ\u201d). To obtain this distribution information, we automatically label some relevant instances
\nfor 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.\u201cHoffman\u201d,\u201cChad Johnson\u201d, etc.), because our model is trained on both the labeled data sets of queried and other names by mining their shared predictive structure.<\/p>\n","protected":false},"excerpt":{"rendered":"
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.\u201cAZ\u201d) based on the distribution knowledge learned from labeled instances, which are related to other names (e.g.\u201cHoffman\u201d,\u201cChad Johnson\u201d, etc.). The gaps among the distributions of the instances related to different names […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"The COLING 2012 Organizing Committee","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"3089","msr_page_range_end":"3104","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"International Conference on Computational Linguistics (COLING 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