Identifying well-formed biomedical phrases in MEDLINE® text.
In the modern world people frequently interact with retrieval systems to satisfy their information needs. Humanly understandable well-formed phrases represent a crucial interface between humans and the web, and the ability to index and search with such phrases is beneficial for human-web interaction...
| Publicado en: | Journal of Biomedical Informatics Vol. 45; no. 6; pp. 1035 - 1042 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Academic Press Inc.
Dec2012
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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=104386075&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104386075 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Dec2012 vid: 45 iid: 6 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 104386075 NLM22683889 2011761256 10.1016/j.jbi.2012.05.005 NLM22683889 PMC3465642 104386075 ppf: 1035 ppct: 7 formats: tig: atl: Identifying well-formed biomedical phrases in MEDLINE® text. aug: au: Kim W Yeganova L Comeau DC Wilbur WJ Kim, Won Yeganova, Lana Comeau, Donald C Wilbur, W John affil: National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA sug: subj: Medline Vocabulary, Controlled Algorithms Information Retrieval Methods Natural Language Processing Software United States ab: In the modern world people frequently interact with retrieval systems to satisfy their information needs. Humanly understandable well-formed phrases represent a crucial interface between humans and the web, and the ability to index and search with such phrases is beneficial for human-web interactions. In this paper we consider the problem of identifying humanly understandable, well formed, and high quality biomedical phrases in MEDLINE documents. The main approaches used previously for detecting such phrases are syntactic, statistical, and a hybrid approach combining these two. In this paper we propose a supervised learning approach for identifying high quality phrases. First we obtain a set of known well-formed useful phrases from an existing source and label these phrases as positive. We then extract from MEDLINE a large set of multiword strings that do not contain stop words or punctuation. We believe this unlabeled set contains many well-formed phrases. Our goal is to identify these additional high quality phrases. We examine various feature combinations and several machine learning strategies designed to solve this problem. A proper choice of machine learning methods and features identifies in the large collection strings that are likely to be high quality phrases. We evaluate our approach by making human judgments on multiword strings extracted from MEDLINE using our methods. We find that over 85% of such extracted phrase candidates are humanly judged to be of high quality. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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