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

  • Authors:
  • Congmin Min;Munirathnam Srikanth;Abraham Fowler

  • Affiliations:
  • Language Computer Corporation, Richardson, TX;Language Computer Corporation, Richardson, TX;Language Computer Corporation, Richardson, TX

  • Venue:
  • SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
  • Year:
  • 2007

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Abstract

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.