Improving Data Driven Wordclass Tagging by System Combination
<|reference_start|>Improving Data Driven Wordclass Tagging by System Combination: 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.<|reference_end|>
@article{van halteren1998improving,
    title={Improving Data Driven Wordclass Tagging by System Combination},
    author={Hans van Halteren (University of Nijmegen), Jakub Zavrel, Walter
  Daelemans (Tilburg University)},
    journal={Proceedings of the 17th International Conference on Computational
  Linguistics (COLING-ACL'98)},
    year={1998},
    archivePrefix={arXiv},
    eprint={cmp-lg/9807013},
    primaryClass={cmp-lg cs.CL}
}