Ahn, David ;
Fissaha Adafre, Sisay ;
de Rijke, Maarten
Towards Task-Based Temporal Extraction and Recognition
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.
BibTeX - Entry
@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.dagstuhl.de/opus/volltexte/2005/315},
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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Keywords: |
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Information extraction, natural language, temporal reasoning, text mining |
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Seminar: |
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05151 - Annotating, Extracting and Reasoning about Time and Events
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Issue date: |
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2005 |
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Date of publication: |
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15.11.2005 |
15.11.2005
