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
Extraction and exploration of spatio-temporal information in documents
Proceedings of the 6th Workshop on Geographic Information Retrieval
Timely YAGO: harvesting, querying, and visualizing temporal knowledge from Wikipedia
Proceedings of the 13th International Conference on Extending Database Technology
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Temporal expressions are one of the important structures in natural language. In order to understand text, temporal expressions have to be identified and normalized by providing ISO-based values. In this paper we present a shallow approach for automatic recognition of temporal expressions based on a supervised machine learning approach trained on an annotated corpus for temporal information, namely TimeBank. Our experiments demonstrate a performance level comparable to a rule-based implementation and achieve the scores of 0.872, 0.836 and 0.852 for precision, recall and F1-measure for the detection task respectively, and 0.866, 0.796, 0.828 when an exact match is required.