A Semi-Supervised Approach to Improve Classification of Infrequent Discourse Relations Using Feature Vector Extension

Several recent discourse parsers have employed fully-supervised machine learning approaches. These methods require human annotators to beforehand create an extensive training corpus, which is a time-consuming and costly process. On the other hand, unlabeled data is abundant and cheap to collect. In this paper, we propose a novel semi-supervised method for discourse relation classification based on the analysis of cooccurring features in unlabeled data, which is then taken into account for extending the feature vectors given to a classifier. Our experimental results on the RST Discourse Treebank corpus and Penn Discourse Treebank indicate that the proposed method brings a significant improvement in classification accuracy and macro-average F-score when small training datasets are used. For instance, with training sets of c.a. 1000 labeled instances, the proposed method brings improvements in accuracy and macro-average F-score up to 50% compared to a baseline classifier. We believe that...

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Hugo Hernault, Danushka Bollegala, Mitsuru Ishizuka
• Keywords: Discourse, Discourse Treebank, EMNLP 2010, Macro-average F-score, Natural Language Processing