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COLING
2002

Efficient Support Vector Classifiers for Named Entity Recognition

9 years 2 months ago
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.
Hideki Isozaki, Hideto Kazawa
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2002
Where COLING
Authors Hideki Isozaki, Hideto Kazawa
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