Abstract
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.
The work is supported by grants from the National Natural Science Foundation of China (#61272361, #61250010).
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Fu, H., Niu, Z., Zhang, C., Wang, L., Jiang, P., Zhang, J. (2013). Classification of Opinion Questions. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_67
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DOI: https://doi.org/10.1007/978-3-642-36973-5_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
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