DagSemProc.05151.12.pdf
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Towards Task-Based Temporal Extraction and Recognition
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Dagstuhl Seminar Proceedings, Volume 5151
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2005-11-15
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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
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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