Temporal relations between events is a valuable source of information which can be used in a large number of natural language processing applications such as question answering, summarization, and information extraction. Supervised temporal relation classification requires large corpora which are difficult, time consuming, and expensive to produce. Active learning strategies are well-suited to reduce this effort by efficiently selecting the most informative samples for labeling. This paper presents novel active learning strategies based on support vector machines (SVM) for temporal relation classification. A large number of empirical comparisons of different active learning algorithms and various kernel functions in SVM shows that proposed active learning strategies are effective for the given task.
['Seyed Abolghasem Mirroshandel', 'Gholamreza Ghassem-Sani', 'Alexis Nasr']
Active Learning Strategies for Support Vector Machines, Application to Temporal Relation Classification