Title: A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously,
Abstract: Capabilities of detecting temporal relations between two events can benefit
many applications. Most of existing temporal relation classifiers were trained
in a supervised manner. Instead, we explore the observation that regular event
pairs show a consistent temporal relation despite of their various contexts,
and these rich contexts can be used to train a contextual temporal relation
classifier, which can further recognize new temporal relation contexts and
identify new regular event pairs. We focus on detecting after and before
temporal relations and design a weakly supervised learning approach that
extracts thousands of regular event pairs and learns a contextual temporal
relation classifier simultaneously. Evaluation shows that the acquired regular
event pairs are of high quality and contain rich commonsense knowledge and
domain specific knowledge. In addition, the weakly supervised trained temporal
relation classifier achieves comparable performance with the state-of-the-art
supervised systems.