Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text

  title={Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text},
  author={Ji Wen and Xu Sun and Xuancheng Ren and Qi Su},
Relation classification is an important semantic processing task in the field of natural language processing. In this paper, we propose the task of relation classification for Chinese literature text. A new dataset of Chinese literature text is constructed to facilitate the study in this task. We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation… Expand
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Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification
  • Ji Wen
  • Computer Science
  • ArXiv
  • 2017
A structure regularization model is proposed to learn relation representations along the SDP extracted from the forest formed by the structure regularized dependency tree, which benefits reducing the complexity of the whole model and helps improve the $F_{1}$ score by 10.3. Expand
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A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
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