MoL-2003-06: Definitional Question Answering Using Trainable Classifiers

MoL-2003-06: Tsur, Oren (2003) Definitional Question Answering Using Trainable Classifiers. [Report]

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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.

Item Type: Report
Report Nr: MoL-2003-06
Series Name: Master of Logic Thesis (MoL) Series
Year: 2003
Uncontrolled Keywords: definitional-questions answering, information retrieval, text mining, text classification, text categorization.
Date Deposited: 12 Oct 2016 14:38
Last Modified: 12 Oct 2016 14:38
URI: https://eprints.illc.uva.nl/id/eprint/744

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