Anchoring floating quantifiers in Japanese-to-English machine translation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Classifier assignment by corpus-based approach
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Classifiers in Japanese-to-English machine translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Corpus-based generation of numeral classifier using phrase alignment
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Web and corpus methods for Malay count classifier prediction
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Implementing the syntax of japanese numeral classifiers
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Building korean classifier ontology based on korean wordnet
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part II
Formalization of ontological relations of korean numeral classifiers
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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In this paper, we present a solution to the problem of generating Japanese numeral classifiers using semantic classes from an ontology. Most nouns must take a numeral classifier when they are quantified in languages such as Chinese, Japanese, Korean, Malay and Thai. In order to select an appropriate classifier, we propose an algorithm which associates classifiers with semantic classes and uses inheritance to list only those classifiers which have to be listed. It generates sortal classifiers with an accuracy of 81%. We reuse the ontology provided by Goi-Taikei---a Japanese lexicon, and show that it is a reasonable choice for this task, requiring information to be entered for less than 6% of individual nouns.