GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text | Zendy

Qile Zhu | Zendy; Xiaolin Li | Zendy; Ana Conesa | Zendy; Cécile Pereira | Zendy

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GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

Author(s) -

Qile Zhu,

Xiaolin Li,

Ana Conesa,

Cécile Pereira

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx815

Subject(s) - computer science , named entity recognition , artificial intelligence , convolutional neural network , feature engineering , context (archaeology) , deep learning , n gram , task (project management) , natural language processing , feature (linguistics) , source code , word (group theory) , code (set theory) , machine learning , language model , paleontology , linguistics , philosophy , management , set (abstract data type) , programming language , economics , biology , operating system

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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