文章基本信息

标题：Arabic Named Entity Recognition: Using Features Extracted from Noisy Data
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作者：Yassine Benajiba ; Imed Zitouni ; Mona Diab 等
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2010
卷号：2010
出版社：ACL Anthology
摘要：Building an accurate Named Entity Recognition (NER) system for languages with complex morphology is a challenging task. In this paper, we present research that explores the feature space using both gold and bootstrapped noisy features to build an improved highly accurate Arabic NER system. We bootstrap noisy features by projection from an Arabic-English parallel corpus that is automatically tagged with a baseline NER system. The feature space covers lexical, morphological, and syntactic features. The proposed approach yields an improvement of up to 1.64 F-measure (absolute).