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题名:
Discriminative learning with natural annotations: Word segmentation as a case study
作者: JiangWenbin ; SunMeng ; Lü ; , Yajuan ; YangYating ; LiuQun
会议名称: 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
会议日期: August 4, 2013 - August 9, 2013
出版日期: 2013
会议地点: Sofia, Bulgaria
出版地: Association for Computational Linguistics (ACL)
收录类别: EI
ISBN: 9781937284503
部门归属: (1) Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, China
摘要: Structural information in web text provides natural annotations for NLP problems such as word segmentation and parsing. In this paper we propose a discriminative learning algorithm to take advantage of the linguistic knowledge in large amounts of natural annotations on the Internet. It utilizes the Internet as an external corpus with massive (although slight and sparse) natural annotations, and enables a classifier to evolve on the large-scaled and real-time updated web text. With Chinese word segmentation as a case study, experiments show that the segmenter enhanced with the Chinese wikipedia achieves significant improvement on a series of testing sets from different domains, even with a single classifier and local features.
语种: 英语
内容类型: 会议论文
URI标识: http://ir.xjipc.cas.cn/handle/365002/3616
Appears in Collections:多语种信息技术研究室_会议论文

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Recommended Citation:
JiangWenbin,SunMeng,Lü,et al. Discriminative learning with natural annotations: Word segmentation as a case study[C]. 见:51st Annual Meeting of the Association for Computational Linguistics, ACL 2013. Sofia, Bulgaria. August 4, 2013 - August 9, 2013.
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