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).
