Our goal is to compare classification performance on temporal relations using traditional features and word embeddings, which have recently shown to achieve good generalisation capabilities in several NLP tasks.
we want to evaluate whether, and in which configurations, they can contribute to advance state-of-the-art performance on temporal relation classification.