Characterizing the Sublanguage of Online Breast Cancer Forums for Medications, Symptoms, and Emotions
Identifieur interne : 000242 ( Pmc/Curation ); précédent : 000241; suivant : 000243Characterizing the Sublanguage of Online Breast Cancer Forums for Medications, Symptoms, and Emotions
Auteurs : Noémie Elhadad [États-Unis] ; Shaodian Zhang [États-Unis] ; Patricia Driscoll [États-Unis] ; Samuel Brody [États-Unis]Source :
- AMIA Annual Symposium Proceedings [ 1942-597X ] ; 2014.
Abstract
Online health communities play an increasingly prevalent role for patients and are the source of a growing body of research. A lexicon that represents the sublanguage of an online community is an important resource to enable analysis and tool development over this data source. This paper investigates a method to generate a lexicon representative of the language of members in a given community with respect to specific semantic types. We experiment with a breast cancer community and detect terms that belong to three semantic types: medications, symptoms and side effects, and emotions. We assess the ability of our automatically generated lexicons to detect new terms, and show that a data-driven approach captures the sublanguage of members in these communities, all the while increasing coverage of general-purpose terminologies. The code and the generated lexicons are made available to the research community.
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PubMed: 25954356
PubMed Central: 4419934
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A lexicon that represents the sublanguage of an online community is an important resource to enable analysis and tool development over this data source. This paper investigates a method to generate a lexicon representative of the language of members in a given community with respect to specific semantic types. We experiment with a breast cancer community and detect terms that belong to three semantic types: medications, symptoms and side effects, and emotions. We assess the ability of our automatically generated lexicons to detect new terms, and show that a data-driven approach captures the sublanguage of members in these communities, all the while increasing coverage of general-purpose terminologies. The code and the generated lexicons are made available to the research community.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">AMIA Annu Symp Proc</journal-id><journal-title-group><journal-title>AMIA Annual Symposium Proceedings</journal-title></journal-title-group><issn pub-type="epub">1942-597X</issn><publisher><publisher-name>American Medical Informatics Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25954356</article-id><article-id pub-id-type="pmc">4419934</article-id><article-id pub-id-type="publisher-id">1985864</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group></article-categories><title-group><article-title>Characterizing the Sublanguage of Online Breast Cancer Forums for Medications, Symptoms, and Emotions</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Elhadad</surname><given-names>Noémie</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="af1-1985864"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Zhang</surname><given-names>Shaodian</given-names></name><xref ref-type="aff" rid="af1-1985864"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Driscoll</surname><given-names>Patricia</given-names></name><xref ref-type="aff" rid="af1-1985864"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Brody</surname><given-names>Samuel</given-names></name><degrees>PhD</degrees><xref ref-type="aff" rid="af2-1985864"><sup>2</sup></xref></contrib></contrib-group><aff id="af1-1985864"><label>1</label>Columbia University, New York, NY</aff><aff id="af2-1985864"><label>2</label>Google, Inc., New York, NY</aff><pub-date pub-type="collection"><year>2014</year></pub-date><pub-date pub-type="epub"><day>14</day><month>11</month><year>2014</year></pub-date><volume>2014</volume><fpage>516</fpage><lpage>525</lpage><permissions><copyright-statement>©2014 AMIA - All rights reserved.</copyright-statement><copyright-year>2014</copyright-year><license><license-p>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose</license-p></license></permissions><abstract><p>Online health communities play an increasingly prevalent role for patients and are the source of a growing body of research. A lexicon that represents the sublanguage of an online community is an important resource to enable analysis and tool development over this data source. This paper investigates a method to generate a lexicon representative of the language of members in a given community with respect to specific semantic types. We experiment with a breast cancer community and detect terms that belong to three semantic types: medications, symptoms and side effects, and emotions. We assess the ability of our automatically generated lexicons to detect new terms, and show that a data-driven approach captures the sublanguage of members in these communities, all the while increasing coverage of general-purpose terminologies. The code and the generated lexicons are made available to the research community.</p></abstract></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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