Semi-supervised topic classification for low resource languages

In this paper, we present a novel methodology for rapidly developing a topic-based document classification system for a language that has limited resources. Our approach, a hybrid one, combines supervised and unsupervised topic classification techniques. Given that access to native speakers is fairly limited for low resource languages, our approach requires annotating only a few broad "root" topics in the corpus. Next, unsupervised topic discovery (UTD) technique is used to automatically determine finer topics within the root topics. Lastly, we use the recently developed unsupervised topic clustering technique to organize the corpus into a hierarchical structure that enables browsing documents at multiple levels of granularity. Recognizing the need for reducing false alarms during runtime, we describe rejection techniques for discarding off-topic documents.