Name
Abstract | ||
|---|---|---|
Automatic disease named entity recognition (NER) plays a fundamental and essential role in knowledge extraction from biomedical literature. In this paper, we proposed a novel integrated model for disease mentions detection using deep network in combination with decoding algorithm and dictionary. To build the network, we implemented Bi-directional LSTM (Long Short-Term Memory) layers to capture long-term context information and fully-connected layers to improve the fitting capability, using concatenation of word embedding trained from raw biomedical texts and character embedding to encode the input. Viterbi algorithm was used to decode the previous output to access initial labeled sequence. On top of that, a disease names dictionary was constructed to label the disease mentions by exact string matching, which provided extra information to optimize the initial output. While training and testing on NCBI disease corpus, our model achieved F-score of 89.58% which performed better than current reported systems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIBM.2017.8217723 | 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | DocType | ISSN |
disease named entity recognition, deep learning, long short-term memory, Viterbi algorithm, dictionary | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Tong | 1 | 0 | 2.03 |
| Zheheng Luo | 2 | 0 | 0.34 |
| Zhao Dongsheng | 3 | 0 | 6.42 |