Cross-domain Text Classification through Iterative Refining of Target Categories Representations

Cross-domain text classification deals with predicting topic labels for documents in a target domain by leveraging knowledge from pre-labeled documents in a source domain, with different terms or different distributions thereof. Methods exist to address this problem by re-weighting documents from the source domain to transfer them to the target one or by finding a common feature space for documents of both domains; they often require the combination of complex techniques, leading to a number of parameters which must be tuned for each dataset to yield optimal performances. We present a simpler method based on creating explicit representations of topic categories, which can be compared for similarity to the ones of documents. Categories representations are initially built from relevant source documents, then are iteratively refined by considering the most similar target documents, with relatedness being measured by a simple regression model based on cosine similarity, built once at the begin. This expectedly leads to obtain accurate representations for categories in the target domain, used to classify documents therein. Experiments on common benchmark text collections show that this approach obtains results better or comparable to other methods, obtained with fixed empirical values for its few parameters.