Unsupervised Domain Adaptation based on Text Relatedness
Georgios Petasis · 2011
In this paper an unsupervised approach to domain adaptation is presented, which exploits external knowledge sources in order to port a classification model into a new thematic domain. Our approach extracts a new feature set from documents of the target domain, and tries to align the new features to the original ones, by exploiting text relatedness from external knowledge sources, such as WordNet. The approach has been evaluated on the task of document classification, involving the classification of newsgroup postings into 20 news groups. 1