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Discriminative learning with natural annotations: Word segmentation as a case study
JiangWenbin; SunMeng; ; , Yajuan; YangYating; LiuQun
Conference Name51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
Conference DateAugust 4, 2013 - August 9, 2013
Conference PlaceSofia, Bulgaria
Publication PlaceAssociation for Computational Linguistics (ACL)

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.

Indexed ByEI
Document Type会议论文
AffiliationKey Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, China
Recommended Citation
GB/T 7714
JiangWenbin,SunMeng,Lü,et al. Discriminative learning with natural annotations: Word segmentation as a case study[C]. Association for Computational Linguistics (ACL),2013:761-769.
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