Character level and word level embedding with bidirectional LSTM - Dynamic recurrent neural network for biomedical named entity recognition from literature.
Named Entity Recognition is the process of identifying different entities in a given context. Biomedical Named Entity Recognition (BNER) is the task of extracting chemical names from biomedical texts to support biomedical and translational research. The aim of the system is to extract useful chemica...
| Publicado en: | Journal of Biomedical Informatics Vol. 112 |
|---|---|
| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Academic Press Inc.
Dec2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147460917&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147460917 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Dec2020 vid: 112 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 147460917 147460917 NLM33122119 10.1016/j.jbi.2020.103609 NLM33122119 147460917 ppct: 1 formats: tig: atl: Character level and word level embedding with bidirectional LSTM - Dynamic recurrent neural network for biomedical named entity recognition from literature. aug: au: Gajendran, Sudhakaran D, Manjula Sugumaran, Vijayan affil: Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, India sug: subj: Communications Media Research, Medical Study Design Short Portable Mental Status Questionnaire ab: Named Entity Recognition is the process of identifying different entities in a given context. Biomedical Named Entity Recognition (BNER) is the task of extracting chemical names from biomedical texts to support biomedical and translational research. The aim of the system is to extract useful chemical names from biomedical literature text without a lot of handcrafted engineering features. This approach introduces a novel neural network architecture with the composition of bidirectional long short-term memory (BLSTM), dynamic recurrent neural network (RNN) and conditional random field (CRF) that uses character level and word level embedding as the only features to identify the chemical entities. Using this approach we have achieved the F1 score of 89.98 on BioCreAtIvE II GM corpus and 90.84 on NCBI corpus by outperforming the existing systems. Our system is based on the deep neural architecture that uses both character and word level embedding which captures the morphological and orthographic information eliminating the need for handcrafted engineering features. The proposed system outperforms the existing systems without a lot of handcrafted engineering features. The embedding concept along with the bidirectional LSTM network proved to be an effective method to identify most of the chemical entities. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|