A Supervised Machine Learning Approach for Temporal Information Extraction
Anup Kumar Kolya, Asif Ekbal, Sivaji Bandyopadhyay · Institutional Repositories DataBase (IRDB) · 2011
Temporal information extraction is an interesting research area in Natural Language Processing (NLP).Here, the main task involves identification of the different relations between various events and time expressions in a document.The relations are then classified into some predefined categories like BEFORE, AFTER, OVERLAP, BEFORE-OR-OVERLAP, OVERLAP-OR-AFTER and VAGUE.In this paper, we report our works of temporal information extraction along the lines of TempEval-2007 evaluation challenge.We adapt supervised machine learning approach for solving the problems of all the three tasks, namely A, B and C. Initially, a baseline system is developed by considering the most frequent temporal relation in the corresponding task's training data.Evaluation results on the TempEval-2007 datasets yield the F-score values of 59.8%, 73.8% and 43.8% for Tasks A, B and C, respectively under the strict evaluation scheme.All these systems show the F-score values of 61.1%, 74.8% and 46.9% for Tasks A, B and C, respectively under the relaxed evaluation scheme.For the sub-ordinate event in Task C, the system shows the F-score values of 55.1% and 56.9% under the strict and relaxed evaluation scheme, respectively.