A transformation-based error-driven learning approach for Chinese temporal information extraction

Authors:
Chunxia Zhang;Cungen Cao;Zhendong Niu;Qing Yang
Affiliations:
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China;School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
Venue:
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Year:
2008

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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.