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

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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

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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.
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