Paper: Efficient Support Vector Classifiers For Named Entity Recognition

ACL ID C02-1054
Title Efficient Support Vector Classifiers For Named Entity Recognition
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2002
Authors

Named Entity (NE) recognition is a task in which proper nouns and numerical information are ex- tracted from documents and are classified into cat- egories such as person, organization, and date. It is a key technology of Information Extraction and Open-Domain Question Answering. First, we show that an NE recognizer based on Support Vector Ma- chines (SVMs) gives better scores than conventional systems. However, off-the-shelf SVM classifiers are too inefficient for this task. Therefore, we present a method that makes the system substantially faster. This approach can also be applied to other simi- lar tasks such as chunking and part-of-speech tag- ging. We also present an SVM-based feature selec- tion method and an efficient training method.