Question Difficulty Estimation in Community Question Answering Services

Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing |

Published by ACL - Association for Computational Linguistics

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In this paper, we address the problem of estimating question difficulty in community question answering services. We propose a competition-based model for estimating question difficulty by leveraging pairwise comparisons between questions and users. Our experimental results show that our model significantly outperforms a PageRank-based approach. Most importantly, our analysis shows that the text of question descriptions reflects the question difficulty. This implies the possibility of predicting question difficulty from the text of question descriptions.