Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation

Topic models can be used in an unsuper-vised domain adaptation for Word Sense Disambiguation (WSD). In the domain adaptation task, three types of topic mod-els are available: (1) a topic model con-structed from the source domain corpus: (2) a topic model constructed from the tar-get 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 do-main have weights describing the similar-ity 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 adap-tation using three domains, we conducted experiments by varying the combination of topic features, and show the effectiveness of the proposed method.