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- W2035534443 abstract "We present a system for identifying and tracking named, nominal, and pronominal mentions of entities within a text document. Our maximum entropy model for mention detection combines two pre-existing named entity taggers (built to extract different entity categories) and other syntactic and morphological feature streams to achieve competitive performance. We developed a novel maximum entropy model for tracking all mentions of an entity within a document. We participated in the Automatic Content Extraction (ACE) evaluation and performed well. We describe our system and present results of the ACE evaluation." @default.
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- W2035534443 date "2003-01-01" @default.
- W2035534443 modified "2023-10-14" @default.
- W2035534443 title "Identifying and tracking entity mentions in a maximum entropy framework" @default.
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- W2035534443 doi "https://doi.org/10.3115/1073483.1073497" @default.
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