Extracting disease-disease associations with context-aware max-margin neural network
碩士 === 國立中央大學 === 軟體工程研究所 === 106 === In our study, we constructed a disease-association corpus then use it to build and evaluate the disease-association extraction system. Finally, we propose a max-margin context-aware neural network. The results show that the support vector machine(SVM) achieves t...
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| Format: | Others |
| Language: | zh-TW |
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2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/tjuz45 |
| Summary: | 碩士 === 國立中央大學 === 軟體工程研究所 === 106 === In our study, we constructed a disease-association corpus then use it to build and evaluate the disease-association extraction system. Finally, we propose a max-margin context-aware neural network. The results show that the support vector machine(SVM) achieves the highest F1-measure of 77.82%. The SVM-based approach is higher than the convolutional neural networks(CNN) by F1-measure of 2.47%. Then we merge the softmax layer of CNN as feature to the SVM then check whether the performance was improved or not. However, the best result is an F1-measure of 77.32%, which is 0.5% lower than the original SVM which using only its feature. The possible reason may be the NN can’t be updated synchronously while training the SVM. Therefore, we constructed a max-margin context-aware neural network to classify disease associations and achieve the highest F1-measure of 84.34%.
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