A Large-Scale Exploration of Effective Global Features for a Joint Entity Detection and Tracking Model

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 97–104, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics A Large-Scale Exploration of Effective Global Features for a Joint Entity Detection and Tracking Model Hal Daum´e III and Daniel Marcu Information Sciences Institute 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 � hdaume,marcu � @isi.edu Abstract Entity detection and tracking (EDT) is the task of identifying textual mentions of real-world entities in documents, ex- tending the named entity detection and coreference resolution task by consider- ing mentions other than names (pronouns, definite descriptions, etc.). Like NE tag- ging and coreference resolution, most so- lutions to the EDT task separate out the mention detection aspect from the corefer- ence aspect. By doing so, these solutions are limited to using only local features for learning. In contrast, by modeling both aspects of the EDT task simultaneously, we are able to learn using highly com- plex, non-local features. We develop a new joint EDT model and explore the util- ity of many features, demonstrating their effectiveness on this task. 1 Introduction In many natural language applications, such as au- tomatic document summarization, machine transla- tion, question answering and information retrieval, it is advantageous to pre-process text documents to identify references to entities. An entity, loosely defined, is a person, location, organization or geo- political entity (GPE) that exists in the real world. Being able to identify references to real-world enti- ties of these types is an important and difficult natu- ral language processing problem. It involves finding text spans that correspond to an entity, identifying what type of entity it is (person, location, etc.), iden- tifying what type of mention it is (name, nominal, pronoun, etc.) and finally identifying which other mentions in the document it corefers with. The dif- ficulty lies in the fact that there are often many am- biguous ways to refer to the same entity. For exam- ple, consider the two sentences below: Bill Clinton NAM PER–1 gave a speech today to the Senate NAM ORG–2 . The President NOM PER–1 outlined his PRO PER–1 plan for budget reform to them PRO ORG–2 . There are five entity mentions in these two sen- tences, each of which is underlined (the correspond- ing mention type and entity type appear as super- scripts and subscripts, respectively, with coreference chains marked in the subscripts), but only two enti- ties: � Bill Clinton, The president, his � and � the Senate, them � . The mention detection task is to identify the entity mentions and their types, without regard for the underlying entity sets, while corefer- ence resolution groups a given mentions into sets. Current state-of-the-art solutions to this problem split it into two parts: mention detection and coref- erence (Soon et al., 2001; Ng and Cardie, 2002; Flo- rian et al., 2004). First, a model is run that attempts to identify each mention in a text and assign it a type (person, organization, …

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