Event-event relation identification: A CRF based approach
Anup Kumar Kolya, Asif Ekbal, Sivaji Bandyopadhyay · 2010
Temporal information extraction is a popular and interesting research field in the area of Natural Language Processing (NLP). The main tasks involve the identification of event-time, event-document creation time and event-event relations in a text. In this paper, we take up Task C that involves identification of relations between the events in adjacent sentences under the TimeML framework. We use a supervised machine learning technique, namely Conditional Random Field (CRF). Initially, a baseline system is developed by considering the most frequent temporal relation in the task's training data. For CRF, we consider only those features that are already available in the TempEval-2007 training set. Evaluation results on the Task C test set yield precision, recall and F-score values of 55.1%, 55.1% and 55.1%, respectively under the strict evaluation scheme and 56.9%, 56.9 and 56.9%, respectively under the relaxed evaluation scheme. Results also show that the proposed system performs better than the baseline system.