Definitional Question-Answering Using Trainable Text Classifiers

Automatic question answering (QA) has gained increasing interest in the last few years. Question-Answering systems return an answer rather than a document. Definitional questions are questions such as Who is Alexander Hamilton? or what are fractals? Looking at logs of web search engines definitional questions occur quite frequently, suggesting it is an important type of questions. Analysis of previous work promotes the hypothesis that the use of a text classifier component improves performance of definitional-QA systems. This thesis serves as a proof of concept that using trainable text classifier improves definitional question answering. I present a naïve heuristic-based QA system, investigate two text classifiers and demonstrate how integrating the text classifiers into definitional-QA system can improve the baseline system.