Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
TIME '07 Proceedings of the 14th International Symposium on Temporal Representation and Reasoning
Automatic time expression labeling for english and chinese text
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Knowledge and reasoning for question answering: Research perspectives
Information Processing and Management: an International Journal
ZamAn and raqm: extracting temporal and numerical expressions in arabic
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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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 tokenbased 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.