Conditional Random Fields for Identifying Appropriate Types of Support for Propositions in Online User Comments

摘要

Park and Cardie (2014) proposed a novel task of automatically identifying appropriate types of support for propositions comprising online user comments, as an essential step toward automated analysis of the adequacy of supporting information. While multiclass Support Vector Machines (SVMs) proved to work reasonably well, they do not exploit the sequential nature of the problem: For instance, verifiable experiential propositions tend to appear together, because a personal narrative typically spans multiple propositions. According to our experiments, however, Conditional Random Fields (CRFs) degrade the overall performance, and we discuss potential fixes to this problem. Nonetheless, we observe that the F_1 score with respect to the unver-ifiable proposition class is increased. Also, semi-supervised CRFs with posterior regular-ization trained on 75% labeled training data can closely match the performance of a supervised CRF trained on the same training data with the remaining 25% labeled as well.