Towards Task-Based Temporal Extraction and Recognition

Ahn, David; Fissaha Adafre, Sisay; de Rijke, Maarten

doi:10.4230/DagSemProc.05151.12

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Towards Task-Based Temporal Extraction and Recognition

Authors David Ahn, Sisay Fissaha Adafre, Maarten de Rijke

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 5151
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2005-11-15

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Document Identifiers

DOI: 10.4230/DagSemProc.05151.12
URN: urn:nbn:de:0030-drops-3150

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Keywords

Information extraction
natural language
temporal reasoning
text mining

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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 Get BibTex

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

Author Details

David Ahn

Sisay Fissaha Adafre

Maarten de Rijke

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