Translation Selection by Combining Multiple Measures for Sense Disambiguation and Word Selection

Translation selection is a process to select, from a set of target language words corresponding to a source language word, the most appropriate one that conveys the correct sense of a source word and makes the target language sentence more natural. In this paper, we propose a hybrid method for translation selection that exploits a bilingual dictionary and a target language corpus. Based on the ‘word-to-sense and sense-to-word’ relationship between a source word and its translations, our method selects translation through two levels: sense disambiguation of a source word and selection of a target word. For translation selection, we introduce three measures: sense preference and sense probability for sense disambiguation, and word probability for word selection. The first one is based on knowledge from a bilingual dictionary, and the others are calculated using statistics from a target language corpus. We evaluated our method and results showed that our method selects more appropriate target words with knowledge extracted from easily obtainable resources.