CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
A neural network framework for relation extraction: Learning entity semantic and relation pattern
Zheng, Suncong1; Xu, Jiaming1; Zhou, Peng1; Bao, Hongyun1; Qi, Zhenyu1; Xu, Bo1,2; bao hongyun
Source PublicationKNOWLEDGE-BASED SYSTEMS
2016-12-15
Volume114Issue:1Pages:12-23
SubtypeArticle
AbstractRelation extraction is to identify the relationship of two given entities in the text. It is an important step in the task of knowledge extraction. Most conventional methods for the task of relation extraction focus on designing effective handcrafted features or learning a semantic representation of the whole sentence. Sentences with the same relationship always share the similar expressions. Besides, the semantic properties of given entities can also help to distinguish some confusing relations. Based on the above observations, we propose a neural network based framework for relation classification. It can simultaneously learn the relation pattern's information and the semantic properties of given entities. In this framework, we explore two specific models: the CNN-based model and LSTM-based model. We conduct experiments on two public datasets: the SemEval-2010 Task8 dataset and the ACE05 dataset. The proposed method achieves the state-of-the-art result without using any external information. Additionally, the experimental results also show that our approach can represent the semantic relationship of the given entities effectively. (C) 2016 Elsevier B.V. All rights reserved.
KeywordRelation Extraction Deep Neural Network Convolutional Neural Network Entity Embedding Keywords Extraction
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.knosys.2016.09.019
Indexed BySCI
Language英语
Funding OrganizationNational High Technology Research and Development Program of China (863 Program)(2015AA015402) ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02070005) ; National Natural Science Foundation(71402178 ; 61602479)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000389396800002
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12344
Collection数字内容技术与服务研究中心_听觉模型与认知计算
Corresponding Authorbao hongyun
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Zheng, Suncong,Xu, Jiaming,Zhou, Peng,et al. A neural network framework for relation extraction: Learning entity semantic and relation pattern[J]. KNOWLEDGE-BASED SYSTEMS,2016,114(1):12-23.
APA Zheng, Suncong.,Xu, Jiaming.,Zhou, Peng.,Bao, Hongyun.,Qi, Zhenyu.,...&bao hongyun.(2016).A neural network framework for relation extraction: Learning entity semantic and relation pattern.KNOWLEDGE-BASED SYSTEMS,114(1),12-23.
MLA Zheng, Suncong,et al."A neural network framework for relation extraction: Learning entity semantic and relation pattern".KNOWLEDGE-BASED SYSTEMS 114.1(2016):12-23.
Files in This Item: Download All
File Name/Size DocType Version Access License
kbs.pdf(1872KB)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
[Zheng, Suncong]'s Articles
[Xu, Jiaming]'s Articles
[Zhou, Peng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zheng, Suncong]'s Articles
[Xu, Jiaming]'s Articles
[Zhou, Peng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zheng, Suncong]'s Articles
[Xu, Jiaming]'s Articles
[Zhou, Peng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: kbs.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

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