Stacked ensemble combined with fuzzy matching for biomedical named entity recognition of diseases.
Biomedical Named Entity Recognition (Bio-NER) is the crucial initial step in the information extraction process and a majorly focused research area in biomedical text mining. In the past years, several models and methodologies have been proposed for the recognition of semantic types related to gene,...
| Publicado en: | Journal of Biomedical Informatics Vol. 64; pp. 1 - 10 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Dec2016
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| 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=119848528&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119848528 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Dec2016 vid: 64 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 119848528 119848528 NLM27634494 119848528 10.1016/j.jbi.2016.09.009 NLM27634494 119848528 ppf: 1 ppct: 9 formats: tig: atl: Stacked ensemble combined with fuzzy matching for biomedical named entity recognition of diseases. aug: au: Bhasuran, Balu Murugesan, Gurusamy Abdulkadhar, Sabenabanu Natarajan, Jeyakumar affil: DRDO-BU Center for Life Sciences, Bharathiar University Campus, Coimbatore 641046, India sug: subj: Disease Bioinformatics Data Mining Algorithms Logic Genes Classification Proteins Human ab: Biomedical Named Entity Recognition (Bio-NER) is the crucial initial step in the information extraction process and a majorly focused research area in biomedical text mining. In the past years, several models and methodologies have been proposed for the recognition of semantic types related to gene, protein, chemical, drug and other biological relevant named entities. In this paper, we implemented a stacked ensemble approach combined with fuzzy matching for biomedical named entity recognition of disease names. The underlying concept of stacked generalization is to combine the outputs of base-level classifiers using a second-level meta-classifier in an ensemble. We used Conditional Random Field (CRF) as the underlying classification method that makes use of a diverse set of features, mostly based on domain specific, and are orthographic and morphologically relevant. In addition, we used fuzzy string matching to tag rare disease names from our in-house disease dictionary. For fuzzy matching, we incorporated two best fuzzy search algorithms Rabin Karp and Tuned Boyer Moore. Our proposed approach shows promised result of 94.66%, 89.12%, 84.10%, and 76.71% of F-measure while on evaluating training and testing set of both NCBI disease and BioCreative V CDR Corpora. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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