Automatic Domain Adaptation for Word Sense Disambiguation Based on Comparison of Multiple Classifiers

Domain adaptation (DA), which involves adapting a classifier developed from source to target data, has been studied intensively in recent years. However, when DA for word sense disambiguation (WSD) was carried out, the optimal DA method varied according to the properties of the source and target data. This paper proposes automatic DA based on comparing the degrees of confidence of multiple classifiers for each instance. We compared three classifiers for three DA methods, where 1) a classifier was trained with a small amount of target data that was randomly selected and manually labeled but without source data, 2) a classifier was trained with source data and a small amount of target data that was randomly selected and manually labeled, and 3) a classifier was trained with selected source data that were sufficiently similar to the target data and a small amount of target data that was randomly selected and manually labeled. We used the method whose degree of confidence was the highest for each instance when Japanese WSD was carried out. The average accuracy of WSD when the DA methods that were determined automatically were used was significantly higher than when the original methods were used collectively.