Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (3): 912-919.doi: 10.13229/j.cnki.jdxbgxb20180042

Previous Articles     Next Articles

Distant supervision for relation extraction with weakconstraints of entity pairs

Dan⁃tong OUYANG1,2(),Jun XIAO1,2,Yu⁃xin YE2()   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun 130012, China
  • Received:2018-01-12 Online:2019-05-01 Published:2019-07-12
  • Contact: Yu?xin YE E-mail:ouyd@jlu.edu.cn;yeyx@jlu.edu.cn

Abstract:

In order to alleviate the false positive problem in distant supervision for relation extraction and improve the precision and recall rate, this paper presents a distant supervision model with weak constraints of entity pairs for relation extraction. This approach first gains constraint information of entity pairs from knowledge base and plain text, which contains key words of entity pairs and entity types. Then the model can obtain features of constraint information automatically by training neural networks. Then these features are used as weak constraints during relation prediction in company with the features of sentences. In contrast experiments, the model with weak constraints of entity pairs achieves higher precision and recall rate. Results show that weak constraints of entity pairs can effectively alleviate the false positive problem and enhance relation extraction.

Key words: artificial intelligence, distant supervision for relation extraction, neural networks, weak constraints of entity pairs, attention mechanism

CLC Number: 

  • TP391

Fig.1

Definition of entity pairs’ key words"

Table 1

Entity types information"

[Neville Chamberlain]:/base/uk_parliament/topic/people/person/soccer/football_player/government/politician
[Germany]:/sports/sports_team_location/film/film_location/base/languages_for_domain_names/topic/location/country/government/government/location/statistical_region/location/location

Fig.2

Processing procedure of weak constraints"

Fig.3

Processing procedure of sentences"

Fig.4

Architecture of attention mechanism withweak constraints"

Fig.5

Result of Held?out evaluation"

Table 2

Result of manual evaluation"

方 法 准确率/%
前100 前200 前500 平均值
Mintz 0.77 0.71 0.55 0.676
MultiR 0.83 0.74 0.59 0.720
MIML 0.85 0.75 0.61 0.737
PCNN+ONE 0.86 0.80 0.69 0.783
PCNN+ATT 0.86 0.81 0.71 0.793
PCNN+ATT+D 0.86 0.82 0.74 0.806
PCNN+ATT+C 0.87 0.83 0.75 0.816
1 Zhou G D , Zhang M , Ji D H , et al . Tree kernel⁃based relation extraction with context⁃sensitive structured parse tree information[C]∥Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning,Prague,Czech Republic,2007:728⁃736.
2 Mintz M , Bills S , Snow R , et al . Distant supervision for relation extraction without labeled data[C]∥Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP,Stroudsburg, PA, USA,2009:1003⁃1011.
3 Riedel S , Yao L M , Mccallum A . Modeling relations and their mentions without labeled text[C]∥European Conference on Machine Learning and Knowledge Discovery in Databases, Barcelona, Spain, 2010:148⁃163.
4 Hoffmann R , Zhang C L , 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,Portland, Oregon, USA, 2011:541⁃550.
5 Surdeanu M , Tibshirani J , Nallapati R , et al . Multi⁃instance multi⁃label learning for relation extraction[C]∥Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language,Jeju Island, Korea,2012:455⁃465.
6 Lin Yan⁃kai , Shen Shi⁃qi , Liu Zhi⁃yuan , et al . Neural relation extraction with selective attention over instances[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics,Berlin, Germany,2016:2124⁃2133.
7 Socher R , Huval B , Manning C D , et al . Semantic compositionality through recursive matrix⁃vector spaces[J/OL].[2017⁃12⁃28].http:∥ai.stanford.edu/~ang/papers/emnlp12⁃SemanticCompositionalityRecursiveMatrixVectorSpaces.pdf.
8 Zeng Dao⁃jian , Liu Kang , Lai Si⁃wei ,et al . Relation classification via convolutional deep neural network[C]∥The 25th International Conference on Computational Linguistics,Dublin,Ireland,2014:2335⁃2344.
9 dos Santos C N , Xiang B , Zhou B . Classifying relations by ranking with convolutional neural networks[C]∥The 7th International Joint Conference on Natural Language Processing,Beijing, China,2015:626⁃634.
10 Zeng Dao⁃jian , Liu Kang , Chen Yu⁃bo , et al . Distant supervision for relation extraction via piecewise convolutional neural networks[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing,Lisbon,Portugal,2015:1753⁃1762.
11 Ji Guo⁃liang , Liu Kang , He Shi⁃zhu ,et al . Distant supervision for relation extraction with sentence⁃level attention and entity descriptions[J/OL].[2017⁃12⁃28].http:∥⁃JiG⁃14491.pdf.
12 Xie R B , Liu Z Y , Sun M S . Representation learning of knowledge graphs with hierarchical types[C]∥Proceedings of the Twenty⁃Fifth International Joint Conference on Artificial Intelligence,New York, USA,2016:2965⁃2971.
13 Bollacker K , Evans C , Paritosh P , et al . Freebase:a collaboratively created graph database for structuring human knowledge[C]∥Proceedings of the ACM SIGMOD International Conference on Management of Data,Vancouver, BC, Canada,2008:1247⁃1250.
14 Auer S , Bizer C , Kobilarov G , et al . DBpedia: a nucleus for a web of open data[J/OL].[2018⁃01⁃04].http:∥.
15 Mikolov T , Chen K , Corrado G , et al . Efficient estimation of word representations in vector space[J/OL].[2018⁃01⁃04]. https:∥.
16 Srivastava N , Hinton G , Krizhevsky A , et al . Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research,2014,15(1):1929⁃1958.
17 Hinton G E , Srivastava N , Krizhevsky A , et al . Improving neural networks by preventing co⁃adaptation of feature detectors[J]. Computer Science,2012,3(4):212⁃223.
[1] DONG Sa, LIU Da-you, OUYANG Ruo-chuan, ZHU Yun-gang, LI Li-na. Logistic regression classification in networked data with heterophily based on second-order Markov assumption [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1571-1577.
[2] GU Hai-jun, TIAN Ya-qian, CUI Ying. Intelligent interactive agent for home service [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1578-1585.
[3] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Measurement of graph similarity based on vertical dimension sequence dynamic time warping method [J]. 吉林大学学报(工学版), 2018, 48(4): 1199-1205.
[4] ZHANG Hao, ZHAN Meng-ping, GUO Liu-xiang, LI Zhi, LIU Yuan-ning, ZHANG Chun-he, CHANG Hao-wu, WANG Zhi-qiang. Human exogenous plant miRNA cross-kingdom regulatory modeling based on high-throughout data [J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[5] HUANG Lan, JI Lin-ying, YAO Gang, ZHAI Rui-feng, BAI Tian. Construction of disease-symptom semantic net for misdiagnosis prompt [J]. 吉林大学学报(工学版), 2018, 48(3): 859-865.
[6] LI Xiong-fei, FENG Ting-ting, LUO Shi, ZHANG Xiao-li. Automatic music composition algorithm based on recurrent neural network [J]. 吉林大学学报(工学版), 2018, 48(3): 866-873.
[7] LIU Jie, ZHANG Ping, GAO Wan-fu. Feature selection method based on conditional relevance [J]. 吉林大学学报(工学版), 2018, 48(3): 874-881.
[8] WANG Xu, OUYANG Ji-hong, CHEN Gui-fen. Heuristic algorithm of all common subsequences of multiple sequences for measuring multiple graphs similarity [J]. 吉林大学学报(工学版), 2018, 48(2): 526-532.
[9] YANG Xin, XIA Si-jun, LIU Dong-xue, FEI Shu-min, HU Yin-ji. Target tracking based on improved accelerated gradient under tracking-learning-detection framework [J]. 吉林大学学报(工学版), 2018, 48(2): 533-538.
[10] LIU Xue-juan, YUAN Jia-bin, XU Juan, DUAN Bo-jia. Quantum k-means algorithm [J]. 吉林大学学报(工学版), 2018, 48(2): 539-544.
[11] QU Hui-yan, ZHAO Wei, QIN Ai-hong. A fast collision detection algorithm based on optimization operator [J]. 吉林大学学报(工学版), 2017, 47(5): 1598-1603.
[12] LI Jia-fei, SUN Xiao-yu. Clustering method for uncertain data based on spectral decomposition [J]. 吉林大学学报(工学版), 2017, 47(5): 1604-1611.
[13] SHAO Ke-yong, CHEN Feng, WANG Ting-ting, WANG Ji-chi, ZHOU Li-peng. Full state based adaptive control of fractional order chaotic system without equilibrium point [J]. 吉林大学学报(工学版), 2017, 47(4): 1225-1230.
[14] WANG Sheng-sheng, WANG Chuang-feng, GU Fang-ming. Spatio-temporal reasoning for OPRA direction relation network [J]. 吉林大学学报(工学版), 2017, 47(4): 1238-1243.
[15] MA Miao, LI Yi-bin. Multi-level image sequences and convolutional neural networks based human action recognition method [J]. 吉林大学学报(工学版), 2017, 47(4): 1244-1252.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] DAI Yan, NIE Shao-feng, ZHOU Tian-hua. Finite element analysis of hysteretic behavior of square steel tube confined steel reinforced concrete column steel frame ring beam joint[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1426 -1435 .
[2] CUI Ling-ling, LU Zhao-yang, LI Jing, LI Yi-hong. Fabric defect recognition algorithm based on Gaussian mixture model in nonsubsampled Contourlet domain[J]. 吉林大学学报(工学版), 2013, 43(03): 734 -739 .
[3] YAO Yun-shi, YAN Qing-qing, WANG Rui-long, SU Pei, CHEN Shi-bin, FENG Zhong-xu. Spraying quality control of vehicle-mounted sprayer for liquid deicing and snow-melting agent[J]. 吉林大学学报(工学版), 2016, 46(1): 120 -125 .
[4] WANG Tao, SAN Xiao-gang, GAO Shi-jie, WANG Hui-xian, WANG Jing, NI Ying-xue. Dynamic characteristics of vertical shaft system of photoelectric turntable[J]. 吉林大学学报(工学版), 2018, 48(4): 1099 -1105 .
[5] TIAN Yan-tao, ZHANG Yu, WANG Xiao-yu, CHEN Hua. Estimation of side-slip angle of electric vehicle based on square-root unscented Kalman filter algorithm[J]. 吉林大学学报(工学版), 2018, 48(3): 845 -852 .
[6] ZHU Jian-feng, ZHANG Jun-yuan, CHEN Xiao-kai, HONG Guang-hui, SONG Zheng-chao, CAO Jie. Design modification for automotive body structure based on seat pull safety performance[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1324 -1330 .
[7] QIN Jing, XU He, PEI Yi-qiang, ZUO Zi-nong, LU Li-li. Influence of initial temperature and initial pressure on premixed laminar burning characteristics of methane-dissociated methanol flames[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1475 -1482 .
[8] LIU Han-bing, SHI Cheng-lin, TAN Guo-jin, WANG Hua, HUANG Bin. Calculation method of the dynamic characteristics of continuous beam bridge with variable cross-section based on staging concept[J]. 吉林大学学报(工学版), 2015, 45(6): 1779 -1783 .
[9] GONG Ya-feng, SHEN Yang-fan, TAN Guo-jin, HAN Chun-peng, HE Yu-long. Unconfined compressive strength of fiber soil with different porosity[J]. 吉林大学学报(工学版), 2018, 48(3): 712 -719 .
[10] BAN Jiao, PEI Chang-Xing. Information entropy theoretic approach to traffic sampling measurement in highspeed IPv6 networks[J]. 吉林大学学报(工学版), 2009, 39(05): 1337 -1341 .