A Combined Approach Using Semantic Role Labelling and Word Sense Disambiguation for Question Generation and Answer Extraction

Most question answering systems are used to predict an expected answer type given a question. In this work, we present a Question Answering System based on the combined approach of Word Sense Disambiguation (WSD) and Semantic Role Labeling (SRL). Our motivation is to generate reasonable questions and solve co-referencing problem extracted from the answer. The proposed model of work is factoid sense based question generation system. We have used Lesk algorithm for WSD and Senna tool for SRL. Based on the sense associated with the sentence, the system generates questions of semantically resolvable. Using deep syntax and semantics analysis, we have extracted an answer from the given question. Hobbs algorithm resolved co-reference problem generated in answer extraction. The experimental results show promising results for the proposed approach.