LREC 2006 - Proceedings sorted by papers

Title	Hand-crafted versus Machine-learned Inflectional Rules: The Euroling-SiteSeeker Stemmer and CST's Lemmatiser
Authors	H. Dalianis, B. Jongejan
Abstract	The Euroling stemmer is developed for a commercial web site and intranet search engine called SiteSeeker. SiteSeeker is basically used in the Swedish domain but to some extent also for the English domain. CST's lemmatiser comes from the Center for Language Technology, University of Copenhagen and was originally developed as a research prototype to create lemmatisation rules from training data. In this paper we compare the performance of the stemmer that uses handcrafted rules for Swedish, Danish and Norwegian as well one stemmer for Greek with CST's lemmatiser that uses training data to extract lemmatisation rules for Swedish, Danish, Norwegian and Greek. The performances of the two approaches are about the same with around 10 percent errors. The handcrafted rule based stemmer techniques are easy to get started with if the programmer has the proper linguistic knowledge. The machine trained sets of lemmatisation rules are very easy to produce without having linguistic knowledge given that one has correct training data.
Keywords
Full paper	Hand-crafted versus Machine-learned Inflectional Rules: The Euroling-SiteSeeker Stemmer and CST's Lemmatiser

Title

Hand-crafted versus Machine-learned Inflectional Rules: The Euroling-SiteSeeker Stemmer and CST's Lemmatiser

Authors

H. Dalianis, B. Jongejan

Abstract

The Euroling stemmer is developed for a commercial web site and intranet search engine called SiteSeeker. SiteSeeker is basically used in the Swedish domain but to some extent also for the English domain. CST's lemmatiser comes from the Center for Language Technology, University of Copenhagen and was originally developed as a research prototype to create lemmatisation rules from training data. In this paper we compare the performance of the stemmer that uses handcrafted rules for Swedish, Danish and Norwegian as well one stemmer for Greek with CST's lemmatiser that uses training data to extract lemmatisation rules for Swedish, Danish, Norwegian and Greek. The performances of the two approaches are about the same with around 10 percent errors. The handcrafted rule based stemmer techniques are easy to get started with if the programmer has the proper linguistic knowledge. The machine trained sets of lemmatisation rules are very easy to produce without having linguistic knowledge given that one has correct training data.

Keywords

Full paper

Hand-crafted versus Machine-learned Inflectional Rules: The Euroling-SiteSeeker Stemmer and CST's Lemmatiser