Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32362
Title: Specification for segmentation and named entity annotation of Chinese classics in the Ming and Qing dynasties
Authors: Xiong, D
Lu, Q 
Lo, F
Shi, D 
Chiu, TS
Li, W
Keywords: Computer-aided annotation
Named entities
Novels in the Ming and Qing dynasties
Segmentation and PoS principles
Semantic analysis
Issue Date: 2013
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2013, v. 7717 LNAI, p. 280-293 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: The quality of text segmentation and annotation plays a significant role in Natural Language Processing especially in downstream applications. This paper presents the specification for word segmentation and named entity annotation targeted for novels in the Ming and Qing dynasties. The purpose of this work is to build the foundational work for computer-aided lexical semantic analysis of classical Chinese literature, especially the transition of Chinese literature from its traditional forms such as traditional verses and vernacular styles to modern Chinese. To assist in literature study, an elaborate named entity annotation scheme is specially developed for classical Chinese. Computer-aided segmentation and named entity annotation are conducted on some famous Ming and Qing Chinese classics. The specification for the segmentation and annotation is produced based on the studies of the morphology and semantics differences as well as similarities between classical Chinese and modern Chinese with reference to the existing standards for modern Chinese processing widely used in Mainland China and Taiwan.
Description: 13th Chinese Lexical Semantics Workshop, CLSW 2012, Wuhan, 6-8 July 2012
URI: http://hdl.handle.net/10397/32362
ISBN: 9783642363368
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-36337-5_29
Appears in Collections:Conference Paper

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