@article{
 title = {Efficient support vector classifiers for named entity recognition},
 type = {article},
 year = {2002},
 identifiers = {[object Object]},
 pages = {1-7},
 volume = {1},
 websites = {http://portal.acm.org/citation.cfm?doid=1072228.1072282},
 publisher = {Association for Computational Linguistics},
 id = {d8379a9a-e175-3285-9c54-c137162cb9e5},
 created = {2012-01-21T12:35:31.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {named entity recognition},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Isozaki2002},
 private_publication = {false},
 abstract = {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.},
 bibtype = {article},
 author = {Isozaki, Hideki and Kazawa, Hideto},
 journal = {Proceedings of the 19th international conference on Computational linguistics}
}

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