LCC-TE: a hybrid approach to temporal relation identification in news text

This paper explores a hybrid approach to temporal information extraction within the TimeML framework. Particularly, we focus on our initial efforts to apply machine learning techniques to identify temporal relations as defined in a constrained manner by the TempEval-2007 task. We explored several machine learning models and human rules to infer temporal relations based on the features available in TimeBank, as well as a number of other features extracted by our in-house tools. We participated in all three sub-tasks of the TempEval task in SemEval-2007 workshop and the evaluation shows that we achieved comparable results in Task A & B and competitive results in Task C.