Efficient Support Vector Classifiers for Named Entity Recognition (2002)
| Venue: | In Proceedings of the 19th International Conference on Computational Linguistics (COLING’02 |
| Citations: | 27 - 1 self |
BibTeX
@INPROCEEDINGS{Isozaki02efficientsupport,
author = {Hideki Isozaki and Hideto Kazawa},
title = {Efficient Support Vector Classifiers for Named Entity Recognition},
booktitle = {In Proceedings of the 19th International Conference on Computational Linguistics (COLING’02},
year = {2002},
pages = {390--396}
}
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Abstract
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.
