Machine Learning
Learning morphological disambiguation rules for Turkish
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Computational Linguistics
Apply a rough set-based classifier to dependency parsing
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
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This paper presents an approach to dependency parsing which can utilize any standard machine learning (classification) algorithm. A decision list learner was used in this work. The training data provided in the form of a treebank is converted to a format in which each instance represents information about one word pair, and the classification indicates the existence, direction, and type of the link between the words of the pair. Several distinct models are built to identify the links between word pairs at different distances. These models are applied sequentially to give the dependency parse of a sentence, favoring shorter links. An analysis of the errors, attribute selection, and comparison of different languages is presented.