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Chinese medical relation extraction based on multi-hop self-attention mechanism

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Abstract

The medical literature is the most important way to demonstrate academic achievements and academic exchanges. Massive medical literature has become a huge treasure trove of knowledge. It is necessary to automatically extract implicit medical knowledge from the medical literature. Medical relation extraction aims to automatically extract medical relations from the medical text for various medical researches. However, there are a few kinds of research in Chinese medical literature. Currently, the popular methods are based on neural networks, which focus on semantic information on one aspect of the sentence. However, complex semantic information in the sentence determines the relation between entities, the semantic information cannot be represented by one sentence vector. In this paper, we propose an attention-based model to extract the multi-aspect semantic information for the Chinese medical relation extraction by multi-hop attention mechanism. The model could generate multiple weight vectors for the sentence through each attention step, therefore, we can generate the different semantic representation of a sentence, respectively. Our model is evaluated by using Chinese medical literature from China National Knowledge Infrastructure (CNKI). It achieves an F1 score of 93.19% for therapeutic relation tasks and 73.47% for causal relation tasks.

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Notes

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Abbreviations

NLP:

Natural language processing

CNKI:

China National Knowledge Infrastructure

PPIs:

Protein–protein interactions

DDIs:

Drug–drug interactions

CPIs:

Chemical–protein interactions

CDIs:

Chemical–disease interactions

SAE:

Stacked autoencoder

CNNs:

Convolutional neural networks

RNN:

Recurrent neural network

LSTM:

Long short term memory network

Bi-LSTM:

Bidirectional long short term memory network

SVM:

Support vector machine

McDepCNN:

Multi-channel dependency-based convolutional neural network

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Funding

This work has been supported by the Natural Science Foundation of China (No. 61632011, 61572102). The Postdoctoral Science Foundation of China (No. 2018M641691). The Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK, P.R. China (COGOS-20190001). The funding bodies did not play any role in the design of the study, data collection and analysis, or preparation of the manuscript.

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Tongxuan Zhang, Hongfei Lin, Michael M. Tadesse, Yuqi Ren & Bo Xu

  2. Dalian Minzu University, Dalian, China

    Xiaodong Duan

  3. State Key Laboratory of Cognitive Intelligence, iFLYTEK, Hefei, People’s Republic of China

    Bo Xu

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  1. Tongxuan Zhang
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  2. Hongfei Lin
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  3. Michael M. Tadesse
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  4. Yuqi Ren
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  5. Xiaodong Duan
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  6. Bo Xu
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Correspondence to Hongfei Lin.

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Zhang, T., Lin, H., Tadesse, M.M. et al. Chinese medical relation extraction based on multi-hop self-attention mechanism. Int. J. Mach. Learn. & Cyber. 12, 355–363 (2021). https://doi.org/10.1007/s13042-020-01176-6

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