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Relation Classification via Target-Concentrated Attention CNNs

  • Jizhao Zhu
  • Jianzhong QiaoEmail author
  • Xinxiao Dai
  • Xueqi Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Relation classification is a key natural language processing task that receives much attentions these years. The goal is to assign pre-defined relation labels to the nominal pairs marked in given sentences. It is obvious that different words in a sentence are differentially informative. Moreover, the importance of words is highly relation-dependent, i.e., the same word may be differentially important for different relations. To include sensitivity to this fact, we present a novel model, referred to as TCA-CNN, which takes the attention mechanism at the word level to pay different attention to individual words according to the semantic relation concentrated when constructing the representation of a sentence. Experimental results show that TCA-CNN achieves a comparable performance compared with the state-of-the-art models on the SemEval 2010 relation classification task.

Keywords

Relation classification Convolutional Neural Networks Attention mechanism 

Notes

Acknowledgments

This work is supported by the 973 Program of China under Grant Nos. 2013CB329606 and 2014CB340405, the National Key Research and Development Program of China under Grant No. 2016YFB1000902, the National Natural Science Foundation of China (NSFC) under Grant Nos. 61272177, 61402442, 61572469, 91646120 and 61572473.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jizhao Zhu
    • 1
  • Jianzhong Qiao
    • 1
    Email author
  • Xinxiao Dai
    • 2
  • Xueqi Cheng
    • 3
  1. 1.College of Computer Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.Shenyang Open UniversityShenyangChina
  3. 3.CAS Key Laboratory of Network Data Science and TechnologyInstitute of Computing Technology, Chinese Academy of SciencesBeijingChina

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