Name
Abstract | ||
|---|---|---|
Recently, neural network architectures have outperformed traditional methods in biomedical named entity recognition. Borrowed from innovations in general text NER, these models fail to address two important problems of polysemy and usage of acronyms across biomedical text. We hypothesize that using a fully-contextualized model that uses contextualized representations along with context dependent transition scores in CRF can alleviate this issue and help further boost the tagger’s performance. Our experiments with this architecture have shown to improve state-of-the-art F1 score on 3 widely used biomedical corpora for NER. We also perform analysis to understand the specific cases where our contextualized model is superior to a strong baseline. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-15719-7_15 | european conference on information retrieval |
Field | DocType | Citations |
F1 score,Architecture,Information retrieval,Computer science,Artificial neural network,Named-entity recognition,Polysemy | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashim Gupta | 1 | 0 | 0.34 |
| Pawan Goyal | 2 | 6 | 3.57 |
| Sudeshna Sarkar | 3 | 423 | 210.58 |
| Mahanandeeshwar Gattu | 4 | 14 | 1.72 |