Question Analysis for a Closed Domain Question Answering System

This study describes and evaluates the techniques we developed for the question analysis module of a closed domain Question Answering (QA) system that is intended for high-school students to support their education. Question analysis, which involves analyzing the questions to extract the necessary information for determining what is being asked and how to approach answering it, is one of the most crucial vcomponents of a QA system. Therefore, we propose novel methods for two major problems in question analysis, namely focus extraction and question classification, based on integrating a rule-based and a Hidden Markov Model (HMM) based sequence classification approach, both of which make use of the dependency relations among the words in the question. Comparisons of these solutions with baseline models are also provided. This study also offers a manually collected and annotated vgold standard data set for further research in this area.