Biomedical negation scope detection with conditional random fields.
Objective: Negation is a linguistic phenomenon that marks the absence of an entity or event. Negated events are frequently reported in both biological literature and clinical notes. Text mining applications benefit from the detection of negation and its scope. However, due to the complexity of langu...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 17; no. 6; pp. 696 - 702 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Nov2010
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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=104933081&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104933081 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Nov2010 vid: 17 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104933081 NLM20962133 2010836436 10.1136/jamia.2010.003228 NLM20962133 PMC3000754 104933081 ppf: 696 ppct: 6 formats: tig: atl: Biomedical negation scope detection with conditional random fields. aug: au: Agarwal S Yu H Agarwal, Shashank Yu, Hong affil: Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA sug: subj: Data Mining Methods Natural Language Processing Human ab: Objective: Negation is a linguistic phenomenon that marks the absence of an entity or event. Negated events are frequently reported in both biological literature and clinical notes. Text mining applications benefit from the detection of negation and its scope. However, due to the complexity of language, identifying the scope of negation in a sentence is not a trivial task.Design: Conditional random fields (CRF), a supervised machine-learning algorithm, were used to train models to detect negation cue phrases and their scope in both biological literature and clinical notes. The models were trained on the publicly available BioScope corpus.Measurement: The performance of the CRF models was evaluated on identifying the negation cue phrases and their scope by calculating recall, precision and F1-score. The models were compared with four competitive baseline systems.Results: The best CRF-based model performed statistically better than all baseline systems and NegEx, achieving an F1-score of 98% and 95% on detecting negation cue phrases and their scope in clinical notes, and an F1-score of 97% and 85% on detecting negation cue phrases and their scope in biological literature.Conclusions: This approach is robust, as it can identify negation scope in both biological and clinical text. To benefit text mining applications, the system is publicly available as a Java API and as an online application at http://negscope.askhermes.org. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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