Detecting hedge cues and their scope in biomedical text with conditional random fields.
Objective: Hedging is frequently used in both the biological literature and clinical notes to denote uncertainty or speculation. It is important for text-mining applications to detect hedge cues and their scope; otherwise, uncertain events are incorrectly identified as factual events. However, due t...
| Publicado en: | Journal of Biomedical Informatics Vol. 43; no. 6; pp. 953 - 962 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Dec2010
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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=104951971&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104951971 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Dec2010 vid: 43 iid: 6 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 104951971 NLM20709188 2010866509 10.1016/j.jbi.2010.08.003 NLM20709188 PMC2991497 104951971 ppf: 953 ppct: 9 formats: tig: atl: Detecting hedge cues and their scope in biomedical text with conditional random fields. aug: au: Agarwal S Yu H Agarwal, Shashank Yu, Hong affil: Medical Informatics, University of Wisconsin-Milwaukee, Milwaukee, WI, USA sug: subj: Algorithms Data Mining Methods Artificial Intelligence Natural Language Processing Information Science Vocabulary, Controlled ab: Objective: Hedging is frequently used in both the biological literature and clinical notes to denote uncertainty or speculation. It is important for text-mining applications to detect hedge cues and their scope; otherwise, uncertain events are incorrectly identified as factual events. However, due to the complexity of language, identifying hedge cues and their scope in a sentence is not a trivial task. Our objective was to develop an algorithm that would automatically detect hedge cues and their scope in biomedical literature.Methodology: We used conditional random fields (CRFs), a supervised machine-learning algorithm, to train models to detect hedge cue phrases and their scope in biomedical literature. The models were trained on the publicly available BioScope corpus. We evaluated the performance of the CRF models in identifying hedge cue phrases and their scope by calculating recall, precision and F1-score. We compared our models with three competitive baseline systems.Results: Our best CRF-based model performed statistically better than the baseline systems, achieving an F1-score of 88% and 86% in detecting hedge cue phrases and their scope in biological literature and an F1-score of 93% and 90% in detecting hedge cue phrases and their scope in clinical notes.Conclusions: Our approach is robust, as it can identify hedge cues and their scope in both biological and clinical text. To benefit text-mining applications, our system is publicly available as a Java API and as an online application at http://hedgescope.askhermes.org. To our knowledge, this is the first publicly available system to detect hedge cues and their scope in biomedical literature. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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