Papers with JNLPBA corpora
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |