Proceedings of the 32nd annual conference on artificial intelligence location:Paderborn, Germany date:15-18 September 2009
Temporal expressions are important structures in natural language. In order to understand text, temporal expressions have to be extracted and normalized. In this paper we present and compare two approaches for the automatic recognition of temporal expressions, based on a supervised machine learning approach and trained on TimeBank. The first approach performs a token-by-token classification and the second one does a binary constituent-based classification of chunk phrases. Our experiments demonstrate that on the TimeBank corpus constituent-based classification performs better than the token-based one. It achieves F1-measure values of 0.852 for the detection task and 0.828 when an exact match is required, which is better than the state-of-the-art results for temporal expression recognition on TimeBank.