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DOI: 10.4230/DagSemProc.05151.12
Towards Task-Based Temporal Extraction and Recognition

Authors: David Ahn, Sisay Fissaha Adafre, and Maarten de Rijke

Published in: Dagstuhl Seminar Proceedings, Volume 5151, Annotating, Extracting and Reasoning about Time and Events (2005)

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.
Cite as

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)

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@InProceedings{ahn_et_al:DagSemProc.05151.12,
  author =	{Ahn, David and Fissaha Adafre, Sisay and de Rijke, Maarten},
  title =	{{Towards Task-Based Temporal Extraction and Recognition}},
  booktitle =	{Annotating, Extracting and Reasoning about Time and Events},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{5151},
  editor =	{Graham Katz and James Pustejovsky and Frank Schilder},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.05151.12},
  URN =		{urn:nbn:de:0030-drops-3150},
  doi =		{10.4230/DagSemProc.05151.12},
  annote =	{Keywords: Information extraction, natural language, temporal reasoning, text mining}
}
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