Addressing Class Imbalance for Improved Recognition of Implicit Discourse Relations
Junyi Jessy Li, Ani Nenkova · 2014
In this paper we address the problem of skewed class distribution in implicit dis-course relation recognition. We examine the performance of classifiers for both bi-nary classification predicting if a particu-lar relation holds or not and for multi-class prediction. We review prior work to point out that the problem has been addressed differently for the binary and multi-class problems. We demonstrate that adopting a unified approach can significantly im-prove the performance of multi-class pre-diction. We also propose an approach that makes better use of the full annotations in the training set when downsampling is used. We report significant absolute im-provements in performance in multi-class prediction, as well as significant improve-ment of binary classifiers for detecting the presence of implicit Temporal, Compari-son and Contingency relations. 1