Syntax and semantics question analysis using user modelling and relevance feedback

Abstract

A Question Answering (QA) system aims to provide relevant answers to users' natural language (NL) questions by consulting its knowledge base (KB). Providing users with the most relevant answers to their questions is an issue. Many answers returned are not relevant to the questions, and this issue is due to many factors. One such factor is the ambiguity yield during the semantic analysis of lexical extracted from the user's question. The existing techniques did not consider some of the terms, called modifier terms, in the user's question which are claimed to have a significant impact of returning the correct answer. The objective of this study is to present the syntax and semantic question analysis using user modelling (UM) and relevance feedback (RF). This analysis interprets all the modifier terms in the user's question in order to yield correct answers. A combination of UM and RF is used to increase the accuracy of the returned answer. UM helps the QA system to understand the user's question and manage for question adjustment. RF provides an extended framework for the QA system to avoid or remedy the ambiguity of the user's question. The analysis utilizes Vector Space Model (VSM) to semantically interpret and correctly converts modifier terms into a quantifiable form. The finding of this analysis demonstrates a good precision percentage of 94.7% in returning relevant answers for each NL question.