Detection of Entity Mentions Occuring in English and Chinese Text

Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), pages 379–386, Vancouver, October 2005. c⃝2005 Association for Computational Linguistics Detection of Entity Mentions Occurring in English and Chinese Text Kadri Hacioglu, Benjamin Douglas and Ying Chen Center for Spoken Language Research University of Colorado at Boulder {hacioglu,benjamin.douglas,yc}@colorado.edu Abstract In this paper, we describe an integrated approach to entity mention detection that yields a monolithic, almost language in- dependent system. It is optimal in the sense that all categorical constraints are si- multaneously considered. The system is compact and easy to develop and main- tain, since only a single set of features and classifiers are needed to be designed and optimized. It is implemented using one- versus-all support vector machine (SVM) classifiers and a number of feature extrac- tors at several linguistic levels. SVMs are well known for their ability to han- dle a large set of overlapping features with theoretically sound generalization proper- ties. Data sparsity might be an impor- tant issue as a result of a large number of classes and relatively moderate train- ing data size. However, we report re- sults that the integrated system performs as good as a pipelined system that decom- poses the problem into a few smaller sub- tasks. We conduct all our experiments us- ing ACE 2004 data, evaluate the systems using ACE metrics and report competitive performance. 1 Introduction The entity-relation (ER) model (Chen, 1976) views the physical world as a collection of entities with complex relationships. Automatic extraction of this model from raw text is important for creat- ing a knowledge base (such as relational databases, marked-up text etc.) that can be used to achieve bet- ter end-to-end performances in several natural lan- guage processing (NLP) applications including in- formation retrieval, question answering and machine translation. For example, in a typical QA system this knowledge base can be used to facilitate extraction of answers and retrieval of relevant documents. Entities and relations in a document can be men- tioned in several different ways. For example, a per- son entity, e.g. Bill Clinton, can be expressed in many different ways such as The President, Presi- dent Clinton, Mr. Clinton, he, him etc. Similarly, one can express a geo-political entity, e.g. United States, as his country or another person entity, e.g. Hillary Clinton, as his wife, and their relation to the entity Bill Clinton as “president-of” and “family”, respectively. It is clear that the detection of these mentions is the first crucial step for the extraction of the ER model to populate a database or an ontology. Extraction of entities and their relationships is usually done in a pipelined system that first iden- tifies entity mentions, next resolves mentions into unique entities (co-reference) and finally finds rela- tions among them (Florian et al., 2004; Kambhatla, 2004). In that architecture, the errors in the first stage propagate and reduce the performance of sub- sequent stages; namely, co-reference resolver, that clusters all different mentions of an entity into …

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