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URN: urn:nbn:de:0030-drops-3150
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Ahn, David ; Fissaha Adafre, Sisay ; de Rijke, Maarten

Towards Task-Based Temporal Extraction and Recognition

05151.AhnDavid.Paper1.315.pdf (0.2 MB)


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.

BibTeX - Entry

  author =	{David Ahn and Sisay Fissaha Adafre and Maarten de Rijke},
  title =	{Towards Task-Based Temporal Extraction and Recognition},
  booktitle =	{Annotating, Extracting and Reasoning about Time and Events},
  year =	{2005},
  editor =	{Graham Katz and James Pustejovsky and Frank Schilder},
  number =	{05151},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{},
  annote =	{Keywords: Information extraction, natural language, temporal reasoning, text mining}

Keywords: Information extraction, natural language, temporal reasoning, text mining
Seminar: 05151 - Annotating, Extracting and Reasoning about Time and Events
Issue Date: 2005
Date of publication: 15.11.2005

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