ArticleBMC medical genomics2017
Disease named entity recognition from biomedical literature using a novel convolutional neural network.
Article in BMC medical genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models.Tribology international · 2023Article
- Constructing a disease database and using natural language processing to capture and standardize free text clinical information.Scientific reports · 2023Article
- Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach.Viruses · 2022Article
- Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts.BMC bioinformatics · 2022Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- Named entity recognition on bio-medical literature documents using hybrid based approach.Journal of ambient intelligence and humanized computing · 2021Article
- Biomedical named entity recognition using deep neural networks with contextual information.BMC bioinformatics · 2019Article
- Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent Neural Network Approach.International journal of environmental research and public health · 2019Article
- BEERE: a web server for biomedical entity expansion, ranking and explorations.Nucleic acids research · 2019Article
- A Domain Knowledge-Enhanced LSTM-CRF Model for Disease Named Entity Recognition.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
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Authors and funding
7 authors.
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Abstract
backgroundAutomatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated.
methodsIn this paper, we propose a novel multiple label convolutional neural network (MCNN) based disease NER approach. In this approach, instead of the CRF layer, a multiple label strategy (MLS) first introduced by us, is employed. First, the character-level embedding, word-level embedding and lexicon feature embedding are concatenated. Then several convolutional layers are stacked over the concatenated embedding. Finally, MLS strategy is applied to the output layer to capture the correlation information between neighboring labels.
resultsAs shown by the experimental results, MCNN can achieve the state-of-the-art performance on both NCBI and CDR corpora.
conclusionsThe proposed MCNN based disease NER method achieves the state-of-the-art performance with little feature engineering. And the experimental results show the MLS strategy's effectiveness of capturing the correlation information between labels in the neighborhood.
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