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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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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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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DOI: https://doi.org/10.1007/s13042-020-01176-6