CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Learning Generalized Features for Semantic Role Labeling
Yang, Haitong1; Zong, Chengqing2
Source PublicationACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
2016-06-01
Volume15Issue:4Pages:28:1-28:16
SubtypeArticle
AbstractThis article makes an effort to improve Semantic Role Labeling (SRL) through learning generalized features. The SRL task is usually treated as a supervised problem. Therefore, a huge set of features are crucial to the performance of SKL systems. But these features often lack generalization powers when predicting an unseen argument. This article proposes a simple approach to relieve the issue. A strong intuition is that arguments occurring in similar syntactic positions are likely to bear the same semantic role, and, analogously, arguments that are lexically similar are likely to represent the same semantic role. Therefore, it will be informative to SRL if syntactic or lexical similar arguments can activate the same feature. Inspired by this, we embed the information of lexicalization and syntax into a feature vector for each argument and then use K -means to make clustering for all feature vectors of training set. For an unseen argument to be predicted, it will belong: to the same cluster as its similar arguments of training set. Therefore, the clusters can be thought of as a kind of generalized feature. We evaluate our method on several benchmarks. The experimental results show that our approach can significantly improve the SRL performance.
KeywordAlgorithms Languages Experimentation Performance Semantic Role Labeling Generalized Features Similar Arguments K-means
WOS HeadingsScience & Technology ; Technology
DOI10.1145/2890496
Indexed BySCI
Language英语
Funding OrganizationNatural Science Foundation of China(61333018) ; Strategic Priority Research Program of the CAS(XDB02070007)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000377298900008
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11856
Collection模式识别国家重点实验室_自然语言处理
Corresponding AuthorZong, Chengqing
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Intelligence Bldg,95,Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, CAS Ctr Excellence Brain Sci & Intelligence Techn, Intelligence Bldg,95,Zhongguancun East Rd, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yang, Haitong,Zong, Chengqing. Learning Generalized Features for Semantic Role Labeling[J]. ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,2016,15(4):28:1-28:16.
APA Yang, Haitong,&Zong, Chengqing.(2016).Learning Generalized Features for Semantic Role Labeling.ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING,15(4),28:1-28:16.
MLA Yang, Haitong,et al."Learning Generalized Features for Semantic Role Labeling".ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 15.4(2016):28:1-28:16.
Files in This Item: Download All
File Name/Size DocType Version Access License
Learning Generalized(348KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yang, Haitong]'s Articles
[Zong, Chengqing]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yang, Haitong]'s Articles
[Zong, Chengqing]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yang, Haitong]'s Articles
[Zong, Chengqing]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Learning Generalized Features for Semantic Role Labeling.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.