Abstract
Temporal information processing plays an important role in many application areas such as information retrieval, question answering, machine translation, and text summarization. This paper proposes a transformation-based error-driven learning approach to extracting temporal expressions from Chinese unstructured texts. The temporal expression annotator used in the approach is developed based on a Chinese time ontology, which includes concepts of temporal expressions and their taxonomical relations. Experiments in three domains show that our algorithm obtained promising results.
The first, third and fourth authors are supported by the Natural Science Foundation (grant no.60705022), the Program for New Century Excellent Talents in Universities of China, and Beijing Institute of Technology Basic Research Foundation (grant no.411002). The second author is supported by the Natural Science Foundation (grant no.60273019, 60496326, 60573063, and 60573064) and the National 973 Program (grant no. 2003CB317008 and G1999032701).
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Zhang, C., Cao, C., Niu, Z., Yang, Q. (2008). A Transformation-Based Error-Driven Learning Approach for Chinese Temporal Information Extraction. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_80
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DOI: https://doi.org/10.1007/978-3-540-68636-1_80
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