Regularized Phrase-Based Topic Model for Automatic Question Classification With Domain-Agnostic Class Labels
S. Supraja, Andy W. H. Khong, Sivanagaraja Tatinati · IEEE/ACM Transactions on Audio Speech and Language Processing · 2021
Classification of questions according to domain-agnostic class labels relies on a suitable feature extraction process. We propose the use of phrases that are more effective than words to represent questions. The proposed phrase-based topic modeling technique employs asymmetric priors that are scaled with a new C-value for nested regular expressions. In addition, to suppress high-frequency words in phrases, we deploy term weightages computed using the modified distinguishing feature selector. The proposed approach also incorporates a new topic regularization mechanism to facilitate efficient mapping of questions to class labels. We validate the performance of the above approach via four datasets across different domain-agnostic class labels comprising question types, reasoning capabilities, and cognitive complexities. Results obtained highlight that the proposed technique outperforms existing methods in terms of macro-average F1 score.