Graphs and algorithms
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Verbal case frame acquisition from a bilingual corpus: gradual knowledge acquisition
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Two methods for learning ALT-J/E translation rules from examples and a semantic hierarchy
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Generalizing case frames using a thesaurus and the MDL principle
Computational Linguistics
A procedure for multi-class discrimination and some linguistic applications
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
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The Decision Tree Learning Algorithms (DTLAs) are getting keen attention from the natural language processing research community, and there have been a series of attempts to apply them to verbal case frame acquisition. However, a DTLA cannot handle structured attributes like nouns, which are classified under a thesaurus. In this paper, we present a new DTLA that can rationally handle the structured attributes. In the process of tree generation, the algorithm generalizes each attribute optimally using a given thesaurus. We apply this algorithm to a bilingual corpus and show that it successfully learned a generalized decision tree for classifying the verb "take" and that the tree was smaller with more prediction power on the open data than the tree learned by the conventional DTLA.