Topic models can be used in an unsupervised domain adaptation for Word Sense Disambiguation (WSD). In the domain adaptation task, three types of topic models are available: (1) a topic model constructed from the source domain corpus: (2) a topic model constructed from the target domain corpus, and (3) a topic model constructed from both domains. Basically, three topic features made from each topic model are added to the normal feature used for WSD. By using the extended features, SVM learns and solves WSD. However, the topic features constructed from source domain have weights describing the similarity between the source corpus and the entire corpus because the topic features made from the source domain can reduce the accuracy of WSD. In six transitions of domain adaptation using three domains, we conducted experiments by varying the combination of topic features, and show the effectiveness of the proposed method.
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