Factorizing Complex Models: A Case Study in Mention Detection

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 473–480, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Factorizing Complex Models: A Case Study in Mention Detection Radu Florian, Hongyan Jing, Nanda Kambhatla and Imed Zitouni IBM TJ Watson Research Center Yorktown Heights, NY 10598 {raduf,hjing,nanda,izitouni}@us.ibm.com Abstract As natural language understanding re- search advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nu- anced word characteristics, more linguistic properties, deeper semantic and syntactic features. One such example, explored in this article, is the mention detection and recognition task in the Automatic Content Extraction project, with the goal of iden- tifying named, nominal or pronominal ref- erences to real-world entities—mentions— and labeling them with three types of in- formation: entity type, entity subtype and mention type. In this article, we investi- gate three methods of assigning these re- lated tags and compare them on several data sets. A system based on the methods presented in this article participated and ranked very competitively in the ACE’04 evaluation. 1 Introduction Information extraction is a crucial step toward un- derstanding and processing natural language data, its goal being to identify and categorize impor- tant information conveyed in a discourse. Exam- ples of information extraction tasks are identifi- cation of the actors and the objects in written text, the detection and classification of the rela- tions among them, and the events they participate in. These tasks have applications in, among other fields, summarization, information retrieval, data mining, question answering, and language under- standing. One of the basic tasks of information extraction is the mention detection task. This task is very similar to named entity recognition (NER), as the objects of interest represent very similar concepts. The main difference is that the latter will identify, however, only named references, while mention de- tection seeks named, nominal and pronominal ref- erences. In this paper, we will call the identified references mentions – using the ACE (NIST, 2003) nomenclature – to differentiate them from entities which are the real-world objects (the actual person, location, etc) to which the mentions are referring to1. Historically, the goal of the NER task was to find named references to entities and quantity refer- ences – time, money (MUC-6, 1995; MUC-7, 1997). In recent years, Automatic Content Extraction evaluation (NIST, 2003; NIST, 2004) expanded the task to also identify nominal and pronominal refer- ences, and to group the mentions into sets referring to the same entity, making the task more compli- cated, as it requires a co-reference module. The set of identified properties has also been extended to include the mention type of a reference (whether it is named, nominal or pronominal), its subtype (a more specific type dependent on the main entity type), and its genericity (whether the entity points to a specific entity, or a generic one2), besides the customary main entity type. To our knowledge, little research has been done in the natural lan- …

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