An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Learning a robust word sense disambiguation model using hypernyms in definition sentences
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Word sense disambiguation using heterogeneous language resources
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
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We submitted four systems to the Japanese dictionary-based lexical-sample task of Senseval-2. They were i) the support vector machine method ii) the simple Bayes method, iii) a method combining the two, and iv) a method combining two kinds of each. The combined methods obtained the best precision among the submitted systems. After the contest, we tuned the parameter used in the simple Bayes method, and it obtained higher precision. An explanation of these systems used in Japanese word sense disambiguation was provided.