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.
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References
Verhagen, M., Gaizauskas, R., Schilder, F., Katz, M.H.G., Pustejovsky, J.: SemEval-2007 Task 15: TempEval Temporal Relation Identification. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval 2007), Prague, pp. 75–80 (2007)
Boguraev, B., Ando, R.K.: TimeMLCompliant Text Analysis for Temporal Reasoning. In: Proceedings of Nineteenth International Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 997–1003 (2005)
Mani, I., Wellner, B., Verhagen, M., Lee, C.M., Pustejovsky, J.: Machine Learning of Temporal Relation. In: Proceedings of the 44th Annual meeting of the Association for Computational Linguistics, Australia (2006)
Chambers, N., Wang, S., Jurafsky, D.: Classifying Temporal Relations between Events. In: Proceedings of the ACL 2007 Demo and Poster Sessions, Prague, Czech Republic, pp. 173–176 (2007)
Mani, I., Wellner, B., Verhagen, M., Pustejovsky, J.: Three Approaches to Learning TLINKs in TimeML. In: Technical Report CS-07-268, Computer Science Department, Brandeis University, USA (2007)
Min, C., Srikanth, M., Fowler, A.: LCC-TE: A Hybrid Approach to Temporal Relation Identification in News Text. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval 2007), Prague, pp. 219–222 (2007)
Pustejovsky, J., Castano, J., Ingria, R., Sauri, R., Gaizauskas, R., Setzer, A., Katz, G., Radev, D.: TimeML: Robust Specification of Event and Temporal Expressions in Text. In: Proceedings of the Fifth International Workshop on Computational Semantics (IWCS-5), Tilburg (2003)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: ICML, pp. 282–289 (2001)
Sha, F., Pereira, F.: Shallow Parsing with Conditional Random Fields. In: Proceedings of NAACL 2003, Canada, pp. 134–141 (2003)
Pustejovsky, J., Hanks, P., SaurI, R., See, A., Gaizauskas, R., Setzer, A., Radev, D., Sundheim, B., Day, D., Ferro, L., Lazo, M.: The TIMEBANK Corpus. In: Proceedings of Corpus Linguistics, Lancaster, pp. 647–656 (2003)
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Kolya, A.K., Ekbal, A., Bandyopadhyay, S. (2010). Event-Time Relation Identification Using Machine Learning and Rules. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_16
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DOI: https://doi.org/10.1007/978-3-642-15760-8_16
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