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Báo cáo khoa học: "Improving Data Driven Wordclass Tagging by System Combination"

Chia sẻ: Nhung Nhung | Ngày: | Loại File: PDF | Số trang:7

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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. ...

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