Compiling large language resources using lexical similarity metrics for domain taxonomy learning
10337Authors
nan
Abstract
AbstractIn 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.
Keywords
Full Content
nan
Labeled Text
AbstractIn 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.
Publication Details
- Paper ID:
- 10337
- Year:
- 2006
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan