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Linguistic theory based contextual evidence mining for statistical Chinese co-reference resolution
Zhao, Jun; Liu, Fei-Fan
Source PublicationJOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
2007-07-01
Volume22Issue:4Pages:608-617
SubtypeArticle
AbstractUnder statistical learning framework, the paper focuses on how to use traditional linguistic findings on anaphora resolution as a guide for mining and organizing contextual features for Chinese co-reference resolution. The main achievements are as follows. (1) In order to simulate '' syntactic and semantic parallelism factor '', we extract '' bags of word form and POS '' feature and '' bag of semes '' feature from the contexts of the entity mentions and incorporate them into the baseline feature set. (2) Because it is too coarse to use the feature of bags of word form, POS tag and seme to determine the syntactic and semantic parallelism between two entity mentions, we propose a method for contextual feature reconstruction based on semantic similarity computation, in order that the reconstructed contextual features could better approximate the anaphora resolution factor of '' Syntactic and Semantic Parallelism Preferences ''. (3) We use an entity-mention-based contextual feature representation instead of isolated word-based contextual feature representation, and expand the size of the contextual windows in addition, in order to approximately simulate '' the selectional restriction factor '' for anaphora resolution. The experiments show that the multi-level contextual features are useful for co-reference resolution, and the statistical system incorporated with these features performs well on the standard ACE datasets.
KeywordNatural Language Processing Information Extraction Co-reference Resolution Anaphora Resolution
WOS HeadingsScience & Technology ; Technology
WOS KeywordCOREFERENCE
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS IDWOS:000248356900015
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9447
Collection09年以前成果
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Jun,Liu, Fei-Fan. Linguistic theory based contextual evidence mining for statistical Chinese co-reference resolution[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2007,22(4):608-617.
APA Zhao, Jun,&Liu, Fei-Fan.(2007).Linguistic theory based contextual evidence mining for statistical Chinese co-reference resolution.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,22(4),608-617.
MLA Zhao, Jun,et al."Linguistic theory based contextual evidence mining for statistical Chinese co-reference resolution".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 22.4(2007):608-617.
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