LREC 2000 - Papers

LREC 2000 2^nd International Conference on Language Resources & Evaluation

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Title Something Borrowed, Something Blue: Rule-based Combination of POS Taggers

Authors Borin Lars (Department of Linguistics, Uppsala University, Box 527, SE–751 20 Uppsala, SWEDEN, Lars.Borin@ling.uu.se)

Keywords Knowledge-Rich NLP, Machine Learning, Multilingual Corpora, Parallel Corpora, POS Tagging

Session Session WO1 - Corpus Tagging

Abstract Linguistically annotated text resources are still scarce for many languages and for many text types, mainly because their creation repre-sents a major investment of work and time. For this reason, it is worthwhile to investigate ways of reusing existing resources in novel ways. In this paper, we investigate how off-the-shelf part of speech (POS) taggers can be combined to better cope with text material of a type on which they were not trained, and for which there are no readily available training corpora. We indicate—using freely avail-able taggers for German (although the method we describe is not language-dependent)—how such taggers can be combined by using linguistically motivated rules so that the tagging accuracy of the combination exceeds that of the best of the individual taggers.

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Title	Something Borrowed, Something Blue: Rule-based Combination of POS Taggers
Authors	Borin Lars (Department of Linguistics, Uppsala University, Box 527, SE–751 20 Uppsala, SWEDEN, Lars.Borin@ling.uu.se)
Keywords	Knowledge-Rich NLP, Machine Learning, Multilingual Corpora, Parallel Corpora, POS Tagging
Session	Session WO1 - Corpus Tagging
Abstract	Linguistically annotated text resources are still scarce for many languages and for many text types, mainly because their creation repre-sents a major investment of work and time. For this reason, it is worthwhile to investigate ways of reusing existing resources in novel ways. In this paper, we investigate how off-the-shelf part of speech (POS) taggers can be combined to better cope with text material of a type on which they were not trained, and for which there are no readily available training corpora. We indicate—using freely avail-able taggers for German (although the method we describe is not language-dependent)—how such taggers can be combined by using linguistically motivated rules so that the tagging accuracy of the combination exceeds that of the best of the individual taggers.