Definitional Question Answering Using Trainable Classifiers
Oren Tsur

Abstract:
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 naive
heuristic-based QA system, investigate two text classifiers and
demonstrate how integrating the text classifiers into definitional-QA
system can improve the baseline system.

Keywords: definitional-questions answering, information retrieval,
text mining, text classification, text categorization.