Efficient Support Vector Classifiers for Named Entity Recognition

Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into categories 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 Machines (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 similar tasks such as chunking and part-of-speech tagging. We also present an SVM-based feature selection method and an efficient training method.

• Type: Journal
• Year: 2002
• Where: COLING
• Authors: Hideki Isozaki, Hideto Kazawa
• Keywords: COLING 2002, COLING 2008, NE Recognizer, Open-Domain Question Answering, Support Vector Machines