Answering Opinion Questions with Random Walks on Graphs
Date
2009-08
Journal Title
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Abstract
Opinion Question Answering (Opinion
QA), which aims to find the authors’ sentimental
opinions on a specific target, is
more challenging than traditional fact-based
question answering problems. To
extract the opinion oriented answers, we
need to consider both topic relevance and
opinion sentiment issues. Current solutions
to this problem are mostly ad-hoc
combinations of question topic information
and opinion information. In this paper,
we propose an Opinion PageRank
model and an Opinion HITS model to fully
explore the information from different relations
among questions and answers, answers
and answers, and topics and opinions.
By fully exploiting these relations,
the experiment results show that our proposed
algorithms outperform several state
of the art baselines on benchmark data set.
A gain of over 10% in F scores is achieved
as compared to many other systems.
Description
Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP
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Conference Paper
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Keywords
QUESTION ANSWER SYSTEMS, SENTIMENT ANALYSIS, RANKING
Citation
Li, F., Tang, Y., Huang, M., & Zhu, X. (2009). Answering Opinion Questions with Random Walks on Graphs. Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, Suntec, SGP. (p. 737–745).