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    <titleInfo>
        <title>Learning the Species of Biomedical Named Entities from Annotated Corpora</title>
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    <name type="personal">
        <namePart type="given">Xinglong</namePart>
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            <title>Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)</title>
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        <name type="personal">
            <namePart type="given">Nicoletta</namePart>
            <namePart type="family">Calzolari</namePart>
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            <namePart type="given">Bente</namePart>
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            <namePart type="given">Jan</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>In biomedical articles, terms with the same surface forms are often used to refer to different entities across a number of model organisms, in which case determining the species becomes crucial to term identification systems that ground terms to specific database identifiers. This paper describes a rule-based system that extracts “species indicating words”, such as human or murine, which can be used to decide the species of the nearby entity terms, and a machine-learning species disambiguation system that was developed on manually species-annotated corpora. Performance of both systems were evaluated on gold-standard datasets, where the machine-learning system yielded better overall results.</abstract>
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%0 Conference Proceedings
%T Learning the Species of Biomedical Named Entities from Annotated Corpora
%A Wang, Xinglong
%A Grover, Claire
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F wang-grover-2008-learning
%X In biomedical articles, terms with the same surface forms are often used to refer to different entities across a number of model organisms, in which case determining the species becomes crucial to term identification systems that ground terms to specific database identifiers. This paper describes a rule-based system that extracts “species indicating words”, such as human or murine, which can be used to decide the species of the nearby entity terms, and a machine-learning species disambiguation system that was developed on manually species-annotated corpora. Performance of both systems were evaluated on gold-standard datasets, where the machine-learning system yielded better overall results.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/460_paper.pdf

Markdown (Informal)

[Learning the Species of Biomedical Named Entities from Annotated Corpora](http://www.lrec-conf.org/proceedings/lrec2008/pdf/460_paper.pdf) (Wang & Grover, LREC 2008)

• Learning the Species of Biomedical Named Entities from Annotated Corpora (Wang & Grover, LREC 2008)

ACL

• Xinglong Wang and Claire Grover. 2008. Learning the Species of Biomedical Named Entities from Annotated Corpora. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).