Abstract
We introduce a method for learning to assign word senses to translation pairs. In our approach, this sense assignment or disambiguation problem is transformed into one on how to navigate through a sense network like WordNet aimed at distinguishing the more adequate senses from others. The method involves automatically constructing classification models for branching nodes in the network, and automatically learning to reject less probable senses, based on the translation characteristics of word senses and semantically-related word groups (e.g., lexicographer files) respectively. At run-time, translation pairs are expanded with their synonyms and sense ambiguity is resolved using a greedy algorithm choosing the most likely branches based on the trained classification models. Evaluation shows that our method significantly outperforms the strong baseline of assigning most frequent sense to the translation pairs and effectively determines suitable word senses for given translation pairs, suggesting the possibility of employing our method as a computer-assisted tool for speeding up the process of lexicography or of using our method to assist machine translation systems in word selection. © 2009 by Tzu-yi Nien, Tsun Ku, Chung-chi Huang, Mei-hua Chen, and Jason S. Chang.