With the increasing growth of opinions on news, services and so on, automatic opinion question answering aims at answering questions involving views of persons, and plays an important role in fields of sentiment analysis and information recommendation. One challenge is that opinion questions may contain different types of question focuses that affect answer extraction, such as holders, comparison and location. In this paper, we build a taxonomy of opinion questions, and propose a hierarchical classification technique to classify opinion questions according to our constructed taxonomy. This technique first uses Bayesian classifier and then employs an approach leveraging semantic similarities between questions. Experimental results show that our approach significantly improves performances over baseline and other related works.
[1]
Feiping Nie,et al.
Learning a Mahalanobis distance metric for data clustering and classification
,
2008,
Pattern Recognit..
[2]
Raymond J. Mooney,et al.
Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
,
2005
.
[3]
Claire Cardie,et al.
Multi-Perspective Question Answering Using the OpQA Corpus
,
2005,
HLT.
[4]
Yao Tianfang.
Research on Chinese Sentiment Question Category Classification
,
2011
.
[5]
Wang Lian-shu.
A study of opinion question sentence classification in Question & Answering system
,
2009
.
[6]
Peng Jiang,et al.
A framework for opinion question answering
,
2010,
2010 6th International Conference on Advanced Information Management and Service (IMS).