Name
Abstract | ||
|---|---|---|
Deep learning has achieved remarkable success in a wide range of domains. However, it has not been comprehensively evaluated as a solution for the task of Chinese biomedical named entity recognition (Bio-NER). The traditional deep-learning approach for the Bio-NER task is usually based on the structure of recurrent neural networks (RNN) and only takes word embeddings into consideration, ignoring the value of character-level embeddings to encode the morphological and shape information. We propose an RNN-based approach, WCP-RNN, for the Chinese Bio-NER problem. Our method combines word embeddings and character embeddings to capture orthographic and lexicosemantic features. In addition, POS tags are involved as a priori word information to improve the final performance. The experimental results show our proposed approach outperforms the baseline method; the highest F-scores for subject and lesion detection tasks reach 90.36 and 90.48% with an increase of 3.10 and 2.60% compared with the baseline methods, respectively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11227-017-2229-x | The Journal of Supercomputing |
Keywords | DocType | Volume |
Bio-NER, RNN-based model, POS tags, Chinese EMRs | Journal | 76 |
Issue | ISSN | Citations |
3 | 1573-0484 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqiang Li | 1 | 88 | 15.53 |
| Shenhe Zhao | 2 | 0 | 0.34 |
| Ji-Jiang Yang | 3 | 232 | 35.53 |
| Zhisheng Huang | 4 | 989 | 95.29 |
| Bo Liu | 5 | 143 | 11.62 |
| Shi Chen | 6 | 3 | 1.47 |
| Hui Pan | 7 | 6 | 2.06 |
| Qing Wang | 8 | 11 | 6.39 |