A Sentiment Based Automatic Question-Answering Framework

Qiaofei Ye, Purdue University

Abstract

With the rapid growth and maturity of Question-Answering (QA) domain, non-factoid QuestionAnswering tasks are in high demand. However, existing Question-Answering systems are either fact-based, or highly keyword related and hard-coded. Moreover, if QA is to become more personable, sentiment of the question and answer should be taken into account. However, there is not much research done in the field of non-factoid Question-Answering systems based on sentiment analysis, that would enable a system to retrieve answers in a more emotionally intelligent way. This study investigates to what extent could prediction of the best answer be improved by adding an extended representation of sentiment information into non-factoid Question-Answering.

Degree

M.Sc.

Advisors

Rayz, Purdue University.

Subject Area

Linguistics|Language|Engineering|Artificial intelligence|Cognitive psychology|Computer science|Logic|Psychology

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