ACL - COLING 1998 , Montreal , Canada , 491 - 497 , 1998 Improving Data

نویسنده

  • Hans van Halteren
چکیده

In this paper we examine how the di erences in modelling between di erent data driven systems performing the same NLP task can be exploited to yield a higher accuracy than the best indi vidual system We do this by means of an ex periment involving the task of morpho syntactic wordclass tagging Four well known tagger gen erators Hidden Markov Model Memory Based Transformation Rules and Maximum Entropy are trained on the same corpus data Af ter comparison their outputs are combined us ing several voting strategies and second stage classi ers All combination taggers outperform their best component with the best combina tion showing a lower error rate than the best individual tagger

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ACL - COLING 1998 , Montreal , Canada , 491 - 497 , 1998 Improving Data Driven

In this paper we examine how the diierences in modelling between diierent 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...

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تاریخ انتشار 2016