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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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