Publication - Combination of Machine Learning Approaches for Error Reduction in POS Tagging

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Combination of Machine Learning Approaches for Error Reduction in POS Tagging

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Type:  
In Proceedings

 

Year: 2004
Authors: Maria Koutsombogera; A. Konstandinidis; Harris Papageorgiou
Editor: Vouros, G.; Panayiotopoulos, Th.
Book title: Hellenic Artificial Intelligence Society: 3rd Hellenic Conference on Artificial Intelligence
Address: Σάμος, Ελλάδα
Organization: Πανεπιστήμιο Αιγαίου
Date: Mάϊος
ISBN: 960-431-910-8
Abstract:
In this paper, we report on recent experiments involving the basic POS tagging task on Greek data. Four POS taggers based on different Machine Learning approaches (Transformation-Based, Memory-Based, Hidden Markov Models and Maximum Entropy) are trained on the same corpus to perform morphosyntactic tagging. Their outputs are first examined on the basis of inter-tagger agreement and then combined to construct an ensemble in order to improve the accuracy. Three types of combination methodologies are examined. Finally, we conclude with a detailed presentation of the results along with some remarks on their limits concerning the reduction in error rate.
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