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Event-Time Relation Identification Using Machine Learning and Rules

  • Anup Kumar Kolya
  • Asif Ekbal
  • Sivaji Bandyopadhyay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6231)

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.

Keywords

temporal relation identification rule-based approach conditional random field TempEval-2007 Task B 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anup Kumar Kolya
    • 1
  • Asif Ekbal
    • 2
  • Sivaji Bandyopadhyay
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Computational LinguisticsHeidelberg UniversityHeidelbergGermany

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