Unsupervised solution post identification from discussion forums

Abstract

Discussion forums have evolved into a dependable source of knowledge to solve common problems. However, only a minority of the posts in discussion forums are solution posts. Identifying solution posts from discussion forums, hence, is an important research problem. In this paper, we present a technique for unsupervised solution post identification leveraging a so far unexplored textual feature, that of lexical correlations between problems and solutions. We use translation models and language models to exploit lexical correlations and solution post character respectively. Our technique is designed to not rely much on structural features such as post metadata since such features are often not uniformly available across forums. Our clustering-based iterative solution identification approach based on the EM-formulation performs favorably in an empirical evaluation, beating the only unsupervised solution identification technique from literature by a very large margin. We also show that our unsupervised technique is competitive against methods that require supervision, outperforming one such technique comfortably. © 2014 Association for Computational Linguistics.