A Hybrid Approach for Event Extraction

 

Anup Kumar Kolya, Asif Ekbal, and Sivaji Bandyopadhyay

 

Computer Science and Engineering Department , Jadavpur University, Kolkata; A. Ekbal is also with Computer science and engineering department,IIT Patna, India (e–mail: anup.kolya@gmail.com, asif.ekbal@gmail.com, sivaji_cse_ju@yahoo.com).

 

Manuscript received November 2, 2010.
Manuscript accepted for publication January 15, 2011.

 

Abstract

Event extraction is a popular and interesting research field in the area of Natural Language Processing (NLP). In this paper, we propose a hybrid approach for event extraction within the TimeML framework. Initially, we develop a machine learning based system based on Conditional Random Field (CRF). But most of the deverbal event nouns are not correctly identified by this machine learning approach. From this observation, we came up with a hybrid approach where we introduce several strategies in conjunction with machine learning. These strategies are based on semantic role–labeling, WordNet and handcrafted rules. Evaluation results on the TempEval–2010 datasets yield the precision, recall and F–measure values of approximately 93.00%, 96.00% and 94.47%, respectively. This is approximately 12% higher F–measure in comparison with the best performing system of SemEval–2010.

Key words: About Event, TimeML, Conditional Random Field, TempEval–2010, WordNet.

 

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