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.
CITATION STYLE
Isozaki, H., & Kazawa, H. (2002). Efficient support vector classifiers for named entity recognition (pp. 1–7). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1072228.1072282
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