On Building an Interpretable Topic Modeling Approach for the Urdu Language

This research is an endeavor to combine deep-learning-based language modeling with classical topic modeling techniques to produce interpretable topics for a given set of documents in Urdu, a low resource language. The existing topic modeling techniques produce a collection of words, often uninterpretable, as suggested topics without integrating them into a semantically correct phrase/sentence. The proposed approach would first build an accurate Part of Speech (POS) tagger for the Urdu Language using a publicly available corpus of many million sentences. Using semantically rich feature extraction approaches including Word2Vec and BERT, the proposed approach, in the next step, would experiment with different clustering and topic modeling techniques to produce a list of potential topics for a given set of documents. Finally, this list of topics would be sent to a labeler module to produce syntactically correct phrases that will represent interpretable topics.