Identifying and tracking entity mentions in a maximum entropy framework

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.