Constructing Multiple Domain Taxonomy for Text Processing Tasks

In recent years large volumes of short text data can be eas- ily collected from platforms such as microblogs and product review sites. Very often the obtained short text data contains several domains, which poses many challenges in effective multi-domain text processing because it is challenging to distinguish among the multiple domains in the text data. The concept of multiple domain taxonomy (MDT) has shown promis- ing performance in processing multi-domain text data. However, MDT has to be constructed manually, which requires much expert knowledge about the relevant domains and is time consuming. To address such is- sues, in this paper, we introduce a semi-automatic method to construct an MDT that only requires a small amount of manual input, in com- bination of an unsupervised method for ranking multi-domain concepts based on semantic relationships learned from unlabeled data. We show that the iteratively-constructed MDT using our semi-automatic method can achieve higher accuracy than existing methods in domain classifica- tion, where the accuracy can be improved by up to 11%.