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

ABSTRACT: Motivation: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. Results:We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, t

SUBMITTER: Zhu Q 

PROVIDER: S-EPMC5925775 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature