1998/07/31 by Hans van Halteren, Jakub Zavrel, Walter Daelemans
Computer Science · #cmp-lg #cs.CL
published as Proceedings of the 17th International Conference on Computational Linguistics (COLING-ACL'98) · 7 pages, LaTeX, uses acl.bst, colacl.sty
arxiv created 1998/07/31 · arxiv updated 2009/11/30
In this paper we examine how the differences in modelling between different data driven systems performing the same NLP task can be exploited to yield a higher accuracy than the best individual system. We do this by means of an experiment involving the task of morpho-syntactic wordclass tagging. Four well-known tagger generators (Hidden Markov Model, Memory-Based, Transformation Rules and Maximum Entropy) are trained on the same corpus data. After comparison, their outputs are combined using several voting strategies and second stage classifiers. All combination taggers outperform their best component, with the best combination showing a 19.1% lower error rate than the best individual tagger.