Chinese text relation extraction based on a multi-channel convolutional neural network

LIANG Yanchun; FANG Ailian

doi:10.3969/j.issn.1000-5641.2021.03.010

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May 2021

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LIANG Yanchun, FANG Ailian. Chinese text relation extraction based on a multi-channel convolutional neural network[J]. Journal of East China Normal University (Natural Sciences), 2021, (3): 96-104. doi: 10.3969/j.issn.1000-5641.2021.03.010

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LIANG Yanchun, FANG Ailian. Chinese text relation extraction based on a multi-channel convolutional neural network[J]. Journal of East China Normal University (Natural Sciences), 2021, (3): 96-104. doi: 10.3969/j.issn.1000-5641.2021.03.010

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Chinese text relation extraction based on a multi-channel convolutional neural network

doi: 10.3969/j.issn.1000-5641.2021.03.010

LIANG Yanchun,
FANG Ailian^,

School of Computer Science and Technology, East China Normal University, Shanghai　200062, China

Received Date: 2020-05-18
Publish Date: 2021-05-01

Abstract

Abstract

This paper presents an end-to-end method for Chinese text relation extraction based on a multi-channel CNN (convolutional neural network). Each channel is stacked with a layered neural network; these channels do not interact during recurrent propagation, which enables a neural network to learn different representations. Considering the nuances of the Chinese language, we employed the attention mechanism to extract the semantic features of a sentence, and then integrate structural information using piecewise average pooling. After the maximum pooling layer, the final representation of the sentence is obtained and a relational score is calculated. Finally, the ranking-loss function is used to replace the cross-entropy function for training. The experimental results show that the MCNN_Att_RL (Multi CNN_Att_RL) model proposed in this paper can effectively improve the precision, recall, and F₁ value of entity relation extraction.
- relation extraction,
- multi-channel CNN,
- attention mechanism,
- Chinese text

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References(19)

References

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