Toward automatic Chinese temporal information extraction
Journal of the American Society for Information Science
Interpreting Tense, Aspect and Time Adverbials: A Compositional, Unified Approach
ICTL '94 Proceedings of the First International Conference on Temporal Logic
Tense trees as the "fine structure" of discourse
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
UMass/Hughes: description of the CIRCUS system used for MUC-5
MUC5 '93 Proceedings of the 5th conference on Message understanding
A model for processing temporal references in Chinese
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Applying machine learning to Chinese temporal relation resolution
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Combining linguistic features with weighted Bayesian classifier for temporal reference processing
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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Conventional information extraction systems cannot effectively mine temporal information. For example, users' queries on how one event is related to another in time could not be handled effectively. For this reason, it is important to capture and deduce temporal knowledge associated with the relevant events. It is generally acknowledged that information extraction cannot be isolated from natural language processing. As Chinese has no tenses, conventional means for finding temporal references based on verb forms no longer apply. In this article we present an approach for formulating and discovering temporal relations in Chinese. A set of rules is devised to map the combinational effects of the temporal indicators (also known as temporal markers, gathered from various grammatical categories) in a sentence to its corresponding temporal relation. To evaluate the proposed algorithm, experiments were conducted using a set of news reports and the results look promising. Problem discussions are also provided. Through this work, we hope to open up new doors for future research in Chinese temporal information extraction and processing.