License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05151.11
URN: urn:nbn:de:0030-drops-3354
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2005/335/
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Boguraev, Branimir ; Ando, Rie Kubota

TimeBank-Driven TimeML Analysis

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05151.BoguraevBranimir.Paper.335.pdf (0.4 MB)

Abstract

The design of TimeML as an expressive language for temporal information brings promises, and challenges; in particular, its representational properties raise the bar for traditional information extraction methods applied to the task of text-to-TimeML analysis. A reference corpus, such as TimeBank, is an
invaluable asset in this situation; however, certain characteristics of
TimeBank---size and consistency, primarily---present challenges of their own. We discuss the design, implementation, and performance of an automatic
TimeML-compliant annotator, trained on TimeBank, and deploying a hybrid
analytical strategy of mixing aggressive finite-state processing over
linguistic annotations with a state-of-the-art machine learning technique
capable of leveraging large amounts of unannotated data. The results we
report are encouraging in the light of a close analysis of TimeBank; at the same time they are indicative of the need for more infrastructure work, especially in the direction of creating a larger and more robust reference corpus.

BibTeX - Entry

@InProceedings{boguraev_et_al:DagSemProc.05151.11,
  author =	{Boguraev, Branimir and Ando, Rie Kubota},
  title =	{{TimeBank-Driven TimeML Analysis}},
  booktitle =	{Annotating, Extracting and Reasoning about Time and Events},
  pages =	{1--22},
  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/2005/335},
  URN =		{urn:nbn:de:0030-drops-3354},
  doi =		{10.4230/DagSemProc.05151.11},
  annote =	{Keywords: TimeML analysis, TimeBank corpus, TimeML-compliant temporal information extraction, finite-state processing, machine learning, corpus analysis}
}

Keywords: TimeML analysis, TimeBank corpus, TimeML-compliant temporal information extraction, finite-state processing, machine learning, corpus analysis
Collection: 05151 - Annotating, Extracting and Reasoning about Time and Events
Issue Date: 2005
Date of publication: 15.11.2005

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