English Gigaword Fourth Edition

Item Name: English Gigaword Fourth Edition
Author(s): Robert Parker, David Graff, Junbo Kong, Ke Chen, Kazuaki Maeda
LDC Catalog No.: LDC2009T13
ISBN: 1-58563-515-4
ISLRN: 874-388-164-791-7
DOI: https://doi.org/10.35111/y4px-6y07
Release Date: May 22, 2009
Member Year(s): 2009
DCMI Type(s): Text
Data Source(s): newswire
Project(s): GALE
Application(s): natural language processing, language modeling, information retrieval
Language(s): English
Language ID(s): eng
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2009T13 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Parker, Robert, et al. English Gigaword Fourth Edition LDC2009T13. Web Download. Philadelphia: Linguistic Data Consortium, 2009.
Related Works: View


This file contains documentation for English Gigaword Fourth Edition, Linguistic Data Consortium (LDC) catalog number LDC2009T13 and isbn 1-58563-515-4.

English Gigaword, now being released in its fourth edition, is a comprehensive archive of newswire text data that has been acquired over several years by the LDC at the University of Pennsylvania. The fourth edition includes all of the contents in English Gigawaord Third Edition (LDC2007T07) plus new data covering the 24-month period of January 2007 through December 2008.

The six distinct international sources of English newswire included in this edition are the following:

  • Agence France-Presse, English Service (afp_eng)
  • Associated Press Worldstream, English Service (apw_eng)
  • Central News Agency of Taiwan, English Service (cna_eng)
  • Los Angeles Times/Washington Post Newswire Service (ltw_eng)
  • New York Times Newswire Service (nyt_eng)
  • Xinhua News Agency, English Service (xin_eng)

New in the Fourth Edition

  • Articles with significant Spanish language content have now been identified and documented.
  • Markup has been simplified and made consistent throughout the corpus.
  • Information structure has been simplified.
  • Character entities have been simplified.


For an example of the data in this corpus, please review this sample file.


This work was supported in part by the Defense Advanced Research Projects Agency, GALE Program Grant No. HR0011-06-1-0003. The content of this publication does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.

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