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TimeBank-Driven TimeML Analysis

Authors: Branimir Boguraev and Rie Kubota Ando

Published in: Dagstuhl Seminar Proceedings, Volume 5151, Annotating, Extracting and Reasoning about Time and Events (2005)

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.

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Branimir Boguraev and Rie Kubota Ando. TimeBank-Driven TimeML Analysis. In Annotating, Extracting and Reasoning about Time and Events. Dagstuhl Seminar Proceedings, Volume 5151, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@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/entities/document/10.4230/DagSemProc.05151.11},
  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}
}

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TimeBank-Driven TimeML Analysis

Abstract

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