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
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