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Towards Task-Based Temporal Extraction and Recognition

Authors David Ahn, Sisay Fissaha Adafre, Maarten de Rijke



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DagSemProc.05151.12.pdf
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David Ahn
Sisay Fissaha Adafre
Maarten de Rijke

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David Ahn, Sisay Fissaha Adafre, and Maarten de Rijke. Towards Task-Based Temporal Extraction and Recognition. In Annotating, Extracting and Reasoning about Time and Events. Dagstuhl Seminar Proceedings, Volume 5151, pp. 1-16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2005)
https://doi.org/10.4230/DagSemProc.05151.12

Abstract

We seek to improve the robustness and portability of temporal information extraction systems by incorporating data-driven techniques. We present two sets of experiments pointing us in this direction. The first shows that machine-learning-based recognition of temporal expressions not only achieves high accuracy on its own but can also improve rule-based normalization. The second makes use of a staged normalization architecture to experiment with machine learned classifiers for certain disambiguation sub-tasks within the normalization task.
Keywords
  • Information extraction
  • natural language
  • temporal reasoning
  • text mining

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