@inproceedings{melz-etal-2006-compiling,
title = "Compiling large language resources using lexical similarity metrics for domain taxonomy learning",
author = "Melz, Ronny and
Ryu, Pum-Mo and
Choi, Key-Sun",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/446_pdf.pdf",
abstract = "In this contribution we present a new methodology to compile large language resources for domain-specific taxonomy learning. We describe the necessary stages to deal with the rich morphology of an agglutinative language, i.e. Korean, and point out a second order machine learning algorithm to unveil term similarity from a given raw text corpus. The language resource compilation described is part of a fully automatic top-down approach to construct taxonomies, without involving the human efforts which are usually required.",
}
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<abstract>In this contribution we present a new methodology to compile large language resources for domain-specific taxonomy learning. We describe the necessary stages to deal with the rich morphology of an agglutinative language, i.e. Korean, and point out a second order machine learning algorithm to unveil term similarity from a given raw text corpus. The language resource compilation described is part of a fully automatic top-down approach to construct taxonomies, without involving the human efforts which are usually required.</abstract>
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%0 Conference Proceedings
%T Compiling large language resources using lexical similarity metrics for domain taxonomy learning
%A Melz, Ronny
%A Ryu, Pum-Mo
%A Choi, Key-Sun
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F melz-etal-2006-compiling
%X In this contribution we present a new methodology to compile large language resources for domain-specific taxonomy learning. We describe the necessary stages to deal with the rich morphology of an agglutinative language, i.e. Korean, and point out a second order machine learning algorithm to unveil term similarity from a given raw text corpus. The language resource compilation described is part of a fully automatic top-down approach to construct taxonomies, without involving the human efforts which are usually required.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/446_pdf.pdf
Markdown (Informal)
[Compiling large language resources using lexical similarity metrics for domain taxonomy learning](http://www.lrec-conf.org/proceedings/lrec2006/pdf/446_pdf.pdf) (Melz et al., LREC 2006)
ACL