关键词: 事件关系, 框架语义, 半监督学习, Tri-Training

Abstract:

As one of natural language processing techniques, event relation detection aims at exploring logical relationship between pairwise events. To solve the problem of lacking enough training data in event relation detection tasks, we propose a novel approach based on Tri-Training to augment the training corpus. We firstly use labeled training data to learn three different classifiers, and then exploit majority voting method to expand training corpus with higher confidence samples, iteratively optimize the model and eventually improve the performance of event relation classification. Experimental results show that compared to other methods, the TriTraining based method achieves 64.3% F1-score over four general semantic relations.

Key words: event relation;frame;semi-supervised learning;Tri-Training