Detection of Entity Mentions Occuring in English and Chinese Text

Kadri Hacioglu, Benjamin Douglas, Ying Chen

Abstract: In this paper, we describe an integrated approach to entity mention detection that yields a monolithic, almost language independent system. It is optimal in the sense that all categorical constraints are simultaneously considered. The system is compact and easy to develop and maintain, 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 extractors at several linguistic levels. SVMs are well known for their ability to handle a large set of overlapping features with theoretically sound generalization properties. Data sparsity might be an important issue as a result of a large number of classes and relatively moderate training data size. However, we report results that the integrated system performs as good as a pipelined system that decomposes the problem into a few smaller sub-tasks. We conduct all our experiments using ACE 2004 data, evaluate the systems using ACE metrics and report competitive performance.