@inproceedings{roberts-etal-2008-combining,
title = "Combining Terminology Resources and Statistical Methods for Entity Recognition: an Evaluation",
author = "Roberts, Angus and
Gaizasukas, Robert and
Hepple, Mark and
Guo, Yikun",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Tapias, Daniel",
booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
month = may,
year = "2008",
address = "Marrakech, Morocco",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/758_paper.pdf",
abstract = "Terminologies and other knowledge resources are widely used to aid entity recognition in specialist domain texts. As well as providing lexicons of specialist terms, linkage from the text back to a resource can make additional knowledge available to applications. Use of such resources is especially pertinent in the biomedical domain, where large numbers of these resources are available, and where they are widely used in informatics applications. Terminology resources can be most readily used by simple lexical lookup of terms in the text. A major drawback with such lexical lookup, however, is poor precision caused by ambiguity between domain terms and general language words. We combine lexical lookup with simple filtering of ambiguous terms, to improve precision. We compare this lexical lookup with a statistical method of entity recognition, and to a method which combines the two approaches. We show that the combined method boosts precision with little loss of recall, and that linkage from recognised entities back to the domain knowledge resources can be maintained.",
}
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<abstract>Terminologies and other knowledge resources are widely used to aid entity recognition in specialist domain texts. As well as providing lexicons of specialist terms, linkage from the text back to a resource can make additional knowledge available to applications. Use of such resources is especially pertinent in the biomedical domain, where large numbers of these resources are available, and where they are widely used in informatics applications. Terminology resources can be most readily used by simple lexical lookup of terms in the text. A major drawback with such lexical lookup, however, is poor precision caused by ambiguity between domain terms and general language words. We combine lexical lookup with simple filtering of ambiguous terms, to improve precision. We compare this lexical lookup with a statistical method of entity recognition, and to a method which combines the two approaches. We show that the combined method boosts precision with little loss of recall, and that linkage from recognised entities back to the domain knowledge resources can be maintained.</abstract>
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%0 Conference Proceedings
%T Combining Terminology Resources and Statistical Methods for Entity Recognition: an Evaluation
%A Roberts, Angus
%A Gaizasukas, Robert
%A Hepple, Mark
%A Guo, Yikun
%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 roberts-etal-2008-combining
%X Terminologies and other knowledge resources are widely used to aid entity recognition in specialist domain texts. As well as providing lexicons of specialist terms, linkage from the text back to a resource can make additional knowledge available to applications. Use of such resources is especially pertinent in the biomedical domain, where large numbers of these resources are available, and where they are widely used in informatics applications. Terminology resources can be most readily used by simple lexical lookup of terms in the text. A major drawback with such lexical lookup, however, is poor precision caused by ambiguity between domain terms and general language words. We combine lexical lookup with simple filtering of ambiguous terms, to improve precision. We compare this lexical lookup with a statistical method of entity recognition, and to a method which combines the two approaches. We show that the combined method boosts precision with little loss of recall, and that linkage from recognised entities back to the domain knowledge resources can be maintained.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/758_paper.pdf
Markdown (Informal)
[Combining Terminology Resources and Statistical Methods for Entity Recognition: an Evaluation](http://www.lrec-conf.org/proceedings/lrec2008/pdf/758_paper.pdf) (Roberts et al., LREC 2008)
ACL