When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05151.12
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 =	{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 =		{},
  URN =		{urn:nbn:de:0030-drops-3150},
  doi =		{10.4230/DagSemProc.05151.12},
  annote =	{Keywords: Information extraction, natural language, temporal reasoning, text mining}

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

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