Semantic Similarity Using Register Linear Question Classification (RLQC) for Question Classification

Question Classification(QC) mainly deals with syntactic parsing for finding the similarity. To improve the accuracy of classification, a semantic similarity approach of a question along with the question dataset is calculated. The semantic similarity of the question is initially achieved by syntactic parsing to extract the noun, verb, adverb, and adjective. However, adjectives and adverbs do give sentences an exact meaning that should also be considered for computing the semantic similarity. The proposed RLQC (Register Linear and Question Classification) model for semantic similarity of questions uses HSO (Hirst and St. Onge) measure with Gloss based measure to enhance the semantic similarity relatedness by considering the Noun, Verb, Adverb and Adjective. The semantic similarity of the question pairs for RLQC is 0.2% higher compared to HSO model. The highest semantic similarity of the proposed model achieves a better accuracy.