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.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8044700 |
| Date | 14 May 2019 |
| Creators | Qiaofei Ye (6636317) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/A_SENTIMENT_BASED_AUTOMATIC_QUESTION-ANSWERING_FRAMEWORK/8044700 |
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