Name
Title | ||
|---|---|---|
Named Entity Recognition from Chinese Adverse Drug Event Reports with Lexical Feature based BiLSTM-CRF and Tri-training. |
Abstract | ||
|---|---|---|
Named Entities Recognition (NER) models are established to extract entities from free-text Chinese Adverse Drug Event (ADE) reports, and through which, ADR-related entities of Reasons for medication, Drugs used and ADR names are recognized automatically into structured format, which can be subsequently used for statistical analysis or other kind of NLP tasks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jbi.2019.103252 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
Adverse drug reaction,Named entity recognition,Chinese natural language processing,Lexical feature based bidirectional long short-term memory,Tri-training | Conditional random field,F1 score,Information retrieval,Computer science,Raw data,Pharmacovigilance,Feature based,Named-entity recognition | Journal |
Volume | ISSN | Citations |
96 | 1532-0464 | 2 |
PageRank | References | Authors |
0.38 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yao chen | 1 | 24 | 9.82 |
| Changjiang Zhou | 2 | 2 | 0.71 |
| Tianxin Li | 3 | 2 | 0.38 |
| Hong Wu | 4 | 14 | 1.07 |
| Xia Zhao | 5 | 31 | 11.44 |
| Kai Ye | 6 | 19 | 5.32 |
| Jun Liao | 7 | 2 | 1.39 |