- Publication Date:
- NSF-PAR ID:
- Journal Name:
- 2022 IEEE 19th Annual Consumer Communications Networking Conference (CCNC)
- Sponsoring Org:
- National Science Foundation
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GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text
Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models.
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Availability and implementation
The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN.
Supplementary data are available at Bioinformatics online.
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