Event-time relation identification using machine learning and rules

Authors:
Anup Kumar Kolya;Asif Ekbal;Sivaji Bandyopadhyay
Affiliations:
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Department of Computational Linguistics, Heidelberg University, Heidelberg, Germany;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
Venue:
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Year:
2010

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Abstract

Temporal information extraction is a popular and interesting research field in the area of Natural Language Processing (NLP). In this paper, we report our works on temporal relation identification within the TimeML framework. We worked on TempEval-2007 Task B that involves identification of relations between events and document creation time. Two different systems, one based on machine learning and the other based on handcrafted rules, are developed. The machine learning system is based on Conditional Random Field (CRF) that makes use of only some of the features available in TimeBank corpus in order to infer temporal relations. The second system is developed using a set of manually constructed handcrafted rules. Evaluation results show that the rule-based system performs better compared to the machine learning based system with the precision, recall and F-score values 75.9%, 75.9% and 75.9%, respectively under the strict evaluation scheme and 77.1%, 77.1% and 77.1%, respectively under the relaxed evaluation scheme. In contrast, CRF based system yields precision, recall and F-score values 74.1%, 73.6% and 73.8%, respectively under the strict evaluation scheme and 75.1%, 74.6% and 74.8%, respectively under the relaxed evaluation scheme.