基于远程监督的多因子人物关系抽取模型

黄杨琛; 贾焰; 甘亮; 徐菁; 黄九鸣; 赫中翮

doi:10.11959/j.issn.1000-436x.2018118

您当前的位置：

首页 >

文章列表页 >

基于远程监督的多因子人物关系抽取模型

学术论文 | 更新时间：2024-06-05

- 基于远程监督的多因子人物关系抽取模型
- Multi-factor person entity relation extraction model based on distant supervision
- 通信学报 2018年39卷第7期页码：103-112
- 作者机构：
  
  1. 国防科技大学计算机学院，湖南长沙 410073
  2. 湖南星汉数智科技有限公司知识图谱研发部，湖南长沙 410205
- 作者简介：
  
  [ "黄杨琛（1991-），女，湖南衡阳人，国防科技大学博士生，主要研究方向为数据挖掘、自然语言处理等。" ]
  [ "贾焰（1960-），女，四川成都人，国防科技大学教授、博士生导师，主要研究方向为社交网络分析、信息安全等。" ]
  [ "甘亮（1977-），男，江西樟树人，国防科技大学讲师，主要研究方向为知识工程、专家系统等。" ]
  [ "徐菁（1989-），女，山东济南人，国防科技大学博士生，主要研究方向为知识工程、文本挖掘等。" ]
  [ "黄九鸣（1981-），男，福建安溪人，国防科技大学讲师，主要研究方向为社交网络分析、信息安全等。" ]
  [ "赫中翮（1991-），男，山西大同人，湖南星汉数智科技有限公司工程师，主要研究方向为信息抽取。" ]
- 基金信息：
  
  国家重点研究发展计划基金资助项目(2016QY03D0601);国家重点研究发展计划基金资助项目(2016QY03D0603);国家自然科学基金资助项目(61502517);湖南省重点研发计划资助项目(2018GK2056)
- DOI：10.11959/j.issn.1000-436x.2018118
  中图分类号： TP391
- 网络出版日期：2018-07，
  
  纸质出版日期：2018-07-25
- 稿件说明：
移动端阅览
黄杨琛, 贾焰, 甘亮, 等. 基于远程监督的多因子人物关系抽取模型[J]. 通信学报, 2018,39(7):103-112.

Yangchen HUANG, Yan JIA, Liang GAN, et al. Multi-factor person entity relation extraction model based on distant supervision[J]. Journal on communications, 2018, 39(7): 103-112.
黄杨琛, 贾焰, 甘亮, 等. 基于远程监督的多因子人物关系抽取模型[J]. 通信学报, 2018,39(7):103-112. DOI： 10.11959/j.issn.1000-436x.2018118.

Yangchen HUANG, Yan JIA, Liang GAN, et al. Multi-factor person entity relation extraction model based on distant supervision[J]. Journal on communications, 2018, 39(7): 103-112. DOI： 10.11959/j.issn.1000-436x.2018118.

摘要

针对远程监督的基本假设过强容易引入噪声数据的问题，提出了一种可以对远程监督自动生成的训练数据去噪的人物实体关系抽取模型。在训练数据生成阶段，通过多示例学习的思想和基于 TF-IDF 的关系指示词发现的方法对远程监督产生的数据进行去噪处理，使训练数据达到人工标注质量。在模型分类器中，提出采用词法特征和句法特征相结合的多因子特征作为关系特征向量用于分类器的学习。在大规模真实数据集上的实验结果表明，所提模型结果优于同类型的关系抽取方法。

Abstract

Aiming at the problem that the basic assumption of distant supervision was too strong and easy to produce noise data

a model of the person entity relation extraction which could automatically filter the training data generated by distant supervision was proposed.For training data generation

the data produced by distant supervision would be filtered by multiple instance learning and the method of TF-IDF-based relation keyword detecting

which tried to make the training data has the manual annotation quality.Furthermore

the model combined lexical and syntactic features to extract the effective relation feature vector from two angles of words and semantics for classifier.The experiment results on large scale real-world datasets show that the proposed model outperforms other relation extraction methods which based on distant supervision.

关键词

Keywords

references

CHINCHOR N , MARSH E . Muc-7 information extraction task definition [C ] // The Seventh Message Understanding Conference (MUC-7),Appendices . 1998 : 359 - 367 .

CHE W , LIU T , LI S . Automatic entity relation extraction [J ] . Journal of Chinese Information Processing , 2005 , 19 ( 2 ): 1 - 6 .

刘绍毓 , 李弼程 , 郭志刚 , 等 . 实体关系抽取研究综述 [J ] . 信息工程大学学报 , 2016 ( 5 ): 541 - 547 .

LIU S Y , LI B C , GUO Z G , et al . Review of entity relation extraction [J ] . Journal of Information Engineering University , 2016 ( 5 ): 541 - 547 .

APPELT D E , HOBBS J R , BEAR J , et al . SRI International FASTUS system:MUC-6 test results and analysis [C ] // The 6th Conference On Message Understanding . 1995 : 237 - 248 .

YANGARBER R , GRISHMAN R . NYU:description of the proteus/PET system as used for MUC-7 ST [C ] // Seventh Message Understanding Conference (MUC-7) . 1998 .

AONE C , RAMOS S M . REES:a large-scale relation and event extraction system [C ] // The Sixth Conference On Applied Natural Language Processing . 2000 : 76 - 83 .

KAMBHATLA N , . Combining lexical,syntactic,and semantic features with maximum entropy models for extracting relations [C ] // The ACL 2004 On Interactive Poster And Demonstration Sessions . 2004 :22.

GUO D Z , JIAN S , JIE Z , et al . Exploring various knowledge in relation extraction [C ] // The 43rd Annual Meeting on Association for Computational Linguistics . 2005 : 427 - 434 .

宁海燕 . 实体关系自动抽取技术的比较研究 [D ] . 哈尔滨:哈尔滨工业大学 , 2010 .

NING H Y . Comparative study of automatic entity relation extraction [D ] . Harbin:Harbin Institute of Technology , 2010 .

WANG L , CAO Z , MELO D G , et al . Relation classification via multi-level attention CNNS [C ] // The 54th Annual Meeting of the Association for Computational Linguistics . 2016 : 1298 - 1307 .

KUMAR S . A survey of deep learning methods for relation extraction [J ] . arXiv preprint,arXiv:1705.03645 , 2017 .

ZHOU P , SHI W , TIAN J , et al . Attention-based bidirectional long short-term memory networks for relation classification [C ] // The 54th Annual Meeting of the Association for Computational Linguistics . 2016 : 207 - 212 .

CARLSON A , BETTERIDGE J , KISIEL B , et al . Toward an architecture for never-ending language learning [C ] // AAAI . 2010 :3.

刘锦文 , 许静 , 张利萍 , 等 . 基于标签传播和主动学习的人物社会关系抽取 [J ] . 计算机工程 , 2017 , 34 ( 2 ): 234 - 240 .

LIU J W , XU J , ZHANG L P , et al . Personal social relation extraction based on label propagation and active learning [J ] . Computer Engineering , 2017 , 34 ( 2 ): 234 - 240 .

秦兵 , 刘安安 , 刘挺 . 无指导的中文开放式实体关系抽取 [J ] . 计算机研究与发展 , 2015 , 52 ( 5 ): 1029 - 1035 .

QIN B , LIU A A , LIU T . Unsupervised Chinese open entity relation extraction [J ] . Journal of Computer Research and Development , 2015 , 52 ( 5 ): 1029 - 1035 .

GASHTEOVSKI K , GEMULLA R , DEL C L . MinIE:minimizing facts in open information extraction [C ] // The 2017 Conference on Empirical Methods in Natural Language Processing . 2017 : 2630 - 2640 .

BOLLACKER K , EVANS C , PARITOSH P , et al . Freebase:a collaboratively created graph database for structuring human knowledge [C ] // The 2008 ACM SIGMOD International Conference on Management of Data . 2008 : 1247 - 1250 .

AUER S , BIZER C , KOBILAROV G , et al . Dbpedia:a nucleus for a web of open data [M ] // The Semantic Web . 2007 : 722 - 735 .

SUCHANEK F M , KASNECI G , WEIKUM G . Yago:a core of semantic knowledge [C ] // The 16th International Conference on World Wide Web . 2007 : 697 - 706 .

MINTZ M , BILLS S , SNOW R , et al . Distant supervision for relation extraction without labeled data [C ] // The Joint Conference of the 47th Annual Meeting of the ACL and The 4th International Joint Conference on Natural Language Processing of the AFNLP . 2009 : 1003 - 1011 .

JI G , LIU K , HE S , et al . Distant supervision for relation extraction with sentence-level attention and entity descriptions [C ] // AAAI . 2017 : 3060 - 3066 .

PERSHINA M , MIN B , XU W , et al . Infusion of labeled data into distant supervision for relation extraction [C ] // The 52nd Annual Meeting of the Association for Computational Linguistics . 2014 : 732 - 738 .

潘云 , 布勒布丽汗·伊沙巴依 , 杨静 , 等 . 利用中文在线资源的远程监督人物关系抽取 [J ] . 小型微型计算机系统 , 2015 , 4 :11.

PAN Y , BULEHULIHAN.YISHABAY , YANG J , et al . Distant supervised personal relation extraction using chinese online resource [J ] . Journal of Chinese Mini-Micro Computer Systems , 2015 , 4 :11.

黄蓓静 , 贺樑 , 杨静 . 远程监督人物关系抽取中的去噪研究 [J ] . 计算机应用与软件 , 2017 , 34 ( 7 ): 11 - 18 .

HUANG B J , HE L , YANG J . Research on noise reduction in distant supervised personal relation extraction [J ] . Computer Application and Software , 2017 , 34 ( 7 ): 11 - 18 .

CRAVEN M , KUMLIEN J . Constructing biological knowledge bases by extracting information from text sources [C ] // ISMB . 1999 : 77 - 86 .

SURDEANU M , TIBSHIRANI J , NALLAPATI R , et al . Multi-instance multi-label learning for relation extraction [C ] // The 2012 Joint Conference On Empirical Methods In Natural Language Processing And Computational Natural Language Learning . 2012 : 455 - 465 .

HOFFMANN R , ZHANG C , LING X , et al . Knowledge-based weak supervision for information extraction of overlapping relations [C ] // The 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies . 2011 : 541 - 550 .

胡宝顺 , 王大玲 , 于戈 , 等 . 基于句法结构特征分析及分类技术的答案提取算法 [J ] . 计算机学报 , 2008 , 31 ( 4 ): 662 - 676 .

HU B S , WANG D L , YU G , et al . An answer extraction algorithm based on syntax structure feature parsing and classification [J ] . Chinese Journal of Computers , 2008 , 31 ( 4 ): 662 - 676 .

郭喜跃 , 何婷婷 , 胡小华 , 等 . 基于句法语义特征的中文实体关系抽取 [J ] . 中文信息学报 , 2014 , 28 ( 6 ): 183 - 189 .

GUO X Y , HE T T , HU X H , et al . Chinese named entity relation extraction based on syntactic and semantic features [J ] . Journal of Chinese Information Processing , 2014 , 28 ( 6 ): 183 - 189 .

浏览量

1237

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于机器学习的加密流量分类研究综述

面向代码重用检测的程序语义分析模型

工业互联网流量分析技术综述

基于溯源图和注意力机制的APT攻击检测模型构建

同态明文-密文矩阵运算及其应用