Master Thesis_042519 - printed version - Qiaofei Ye-final.pdf (823.14 kB)
A SENTIMENT BASED AUTOMATIC QUESTION-ANSWERING FRAMEWORK
With the rapid growth and maturity of Question-Answering (QA) domain, non-factoid Question-Answering 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.
History
Degree Type
- Master of Science
Department
- Computer and Information Technology
Campus location
- West Lafayette