A Multi-Channel Deep Neural Network for Relation Extraction

By Yanping Chen, Kai Wang, Weizhe Yang, Yongbin Qing, Ruizhang Huang, Ping Chen. 2020

30 citations recorded by the source corpus.

The task of relation recognition identifies semantic relationships between two named entities in a sentence. In neural network based models, a convolutional layer is often conducted to extract representative local features of a sentence. The convolution operation is implemented through a whole sentence, without considering the structure of a sentence. Because the task to recognize entity relation is processed in sentence level, many ambiguous phenomena (e.g., polysemy) are influential rather than in a document. Capturing structural information of a sentence is helpful to solve this problem. In this paper, a multi-channel framework is presented, which uses two named entities to divide a sentence into several channels. Each channel is stacked with layered neural networks. These channels do not interact during recurrent propagation, which enables a neural network to learn different representations. In our experiments, it outperforms the widely used position embedding approach. Comparing with the state-of-the-art approaches, its performance shows a meaningful improvement.

Subfields: Artificial Intelligence

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In Artificial Intelligence

  1. Haopeng Ren
  2. Yi Cai
  3. Guohua Wang
  4. Xiaofeng Chen
  5. Qing Li

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