Attention Based R&CNN Medical Question Answering System in Chinese
| Autor: | Yan-Ting Liu, Cheng-Hung Hsu, Huey-Ing Liu, Chih-Chien Ni, Wei-Lin Chen, Wei-Ming Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Feature extraction Text segmentation 02 engineering and technology Field (computer science) 020901 industrial engineering & automation Knowledge extraction 0202 electrical engineering electronic engineering information engineering Key (cryptography) Feature (machine learning) Question answering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ICAIIC |
| DOI: | 10.1109/icaiic48513.2020.9065209 |
| Popis: | This paper proposes a Chinese medical question answering system based on CNN s with self-attention embedded model. The key idea of the proposed framework is first understanding whole sentences via LSTM, obtaining the feature map by the convolution layer of CNN, then further-comprehending of question and answer by self-attention and finally pass through to polling layer of CNN to enhance accuracy. In addition, due to the word segmentation issue in the Chinese especially in medical field, character embedding is applied to enhance the accuracy. The experimental results show that the proposed framework improves the accuracy around 80%. |
| Databáze: | OpenAIRE |
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