文章基本信息

标题：Improving Accuracy in Word Class Tagging through the Combination of Machine Learning Systems
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作者：Hans van Halteren ; Jakub Zavrel ; Walter Daelemans 等
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2001
卷号：27
期号：2
页码：199-229
DOI：10.1162/089120101750300508
语种：English
出版社：MIT Press
摘要：We examine how differences in language models, learned by 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 experiments involving the task of morphosyntactic word class tagging, on the basis of three different tagged corpora. 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. The reduction in error rate varies with the material in question, but can be as high as 24.3% with the LOB corpus.