Paper: A Regularized Competition Model for Question Difficulty Estimation in Community Question Answering Services

ACL ID D14-1118
Title A Regularized Competition Model for Question Difficulty Estimation in Community Question Answering Services
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Estimating questions? difficulty levels is an important task in community question answering (CQA) services. Previous stud- ies propose to solve this problem based on the question-user comparisons extract- ed from the question answering threads. However, they suffer from data sparseness problem as each question only gets a lim- ited number of comparisons. Moreover, they cannot handle newly posted question- s which get no comparisons. In this pa- per, we propose a novel question difficul- ty estimation approach called Regularized Competition Model (RCM), which natu- rally combines question-user comparisons and questions? textual descriptions into a unified framework. By incorporating tex- tual information, RCM can effectively deal with data sparseness problem. We further employ a K-Nearest ...