Abstract
We describe the method for word selection in machine translation. Given an input sentence and a target word in the sentence, our system first estimates the similarity between the input sentence and parallel example sets called “Translation Memory.” It then selects an appropriate translation of the target word by using the example set with the highest similarity. The similarity is calculated using an example-based method and a machine learning model, which assesses the similarity based on the similarity of a string, words to the left and right of the target word in the input sentence, frequencies of content words of the input sentence and those of their translation candidates in bilingual and monolingual corpora in English and Japanese. Given an input sentence and a target word in the sentence, an example-based method is applied to them in the first step. Then, if an appropriate example set is not found, a machine learning model is applied to them. The most appropriate machine learning model is selected for each target word from several machine learning models by a certain method such as cross-validation on the training data. In this paper, we show the advantage of our method and also show that what kinds of information contributed to improving the accuracy based on the results of the second contest on word sense disambiguation, SENSEVAL-2, which was held in Spring, 2001.
