Algorithms for analysing the temporal structure of discourse
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Inferring discourse relations in context
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Automatic TIMEX2 tagging of Korean news
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on Temporal Information Processing
Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
From temporal expressions to temporal information: semantic tagging of news messages
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
A multilingual approach to annotating and extracting temporal information
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
Applying machine learning to Chinese temporal relation resolution
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Temporal Semantics Extraction for Improving Web Search
DEXA '09 Proceedings of the 2009 20th International Workshop on Database and Expert Systems Application
CTEMP: a chinese temporal parser for extracting and normalizing temporal information
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
TASE: a time-aware search engine
Proceedings of the 21st ACM international conference on Information and knowledge management
Exploiting temporal information in Web search
Expert Systems with Applications: An International Journal
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Temporal expressions in texts contain significant temporal information. Understanding temporal information is very useful in many NLP applications, such as information extraction, documents summarization and question answering. Therefore, the temporal expression normalization which is used for transforming temporal expressions to temporal information has absorbed many researchers' attentions. But previous works, whatever the hand-crafted rules-based or the machine-learnt rules-based, all can not address the actual problem about temporal reference in real texts effectively. More specifically, the reference time choosing mechanism employed by these works is not adaptable to the universal implicit times in normalization. Aiming at this issue, we introduce a new reference time choosing mechanism for temporal expression normalization, called reference time dynamic-choosing, which assigns the appropriate reference times to different classes of implicit temporal expressions dynamically when normalizing. And then, the solution to temporal expression defuzzification by scenario dependences among temporal expressions is discussed. Finally, we evaluate the system on a substantial corpus collected by Chinese news articles and obtained more promising results than compared methods.