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 describes how the optimal method for DA was determined depending on these properties using decision tree learning, given a triple of the target word type of WSD, the source data, and the target data, and discusses what properties affected the determination of the best method when Japanese WSD was performed.
The main difference was that REF determined the optimal DA method for each triple of the target word type of WSD, source data, and target data, but this paper determined the method for each instance.
Automatic Determination of a Domain Adaptation Method for Word Sense Disambiguation Using Decision Tree Learning
IJCNLP
Computer Science