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Entity Linking for Tweets

xiaohua liu, yitong li, haocheng wu, ming zhou, furu wei and yi lu

The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013


We study the task of entity linking for Twitter, which tries to associate each mention in a tweet with a corresponding 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 model 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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