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.
Url:
PubMed: 25954356
PubMed Central: 4419934
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<front><div type="abstract" xml:lang="en"><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>
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<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>
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