acl acl2013 acl2013-139 acl2013-139-reference knowledge-graph by maker-knowledge-mining
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Author: Xiaohua Liu ; Yitong Li ; Haocheng Wu ; Ming Zhou ; Furu Wei ; Yi Lu
Abstract: We study the task of entity linking for tweets, which tries to associate each mention in a tweet with a knowledge base entry. Two main challenges of this task are the dearth of information in a single tweet and the rich entity mention variations. To address these challenges, we propose a collective inference method that simultaneously resolves a set of mentions. Particularly, our model integrates three kinds of similarities, i.e., mention-entry similarity, entry-entry similarity, and mention-mention similarity, to enrich the context for entity linking, and to address irregular mentions that are not covered by the entity-variation dictionary. We evaluate our method on a publicly available data set and demonstrate the effectiveness of our method.
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