OLAC Record

Genre Docs Words CharTokens Segments Arabic WB 119 59,696 81,620 4,383 Arabic NW 717 198,621 263,060 8,423 Note that word count is based on the untokenized Arabic source, and token count is based on the tokenized Arabic source. The Arabic word alignment tasks consisted of the following components: * Normalizing tokenized tokens as needed * Identifying different types of links * Identifying sentence segments not suitable for annotation * Tagging unmatched words attached to other words or phrases *Samples* Please view the following samples * English Raw * English Token * Arabic Raw * Arabic Token * Word Alignment *Sponsorship* 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. *Updates* None at this time.
Title:GALE Arabic-English Word Alignment Training Part 1 -- Newswire and Web
Access Rights:Licensing Instructions for Subscription & Standard Members, and Non-Members: http://www.ldc.upenn.edu/language-resources/data/obtaining
Bibliographic Citation:Li, Xuansong, et al. GALE Arabic-English Word Alignment Training Part 1 -- Newswire and Web LDC2014T05. Web Download. Philadelphia: Linguistic Data Consortium, 2014
Contributor:Li, Xuansong
Grimes, Stephen
Ismael, Safa
Strassel, Stephanie
Date (W3CDTF):2014
Date Issued (W3CDTF):2014-03-17
Description:*Introduction* GALE Arabic-English Word Alignment Training Part 1 -- Newswire and Web was developed by the Linguistic Data Consortium (LDC) and contains 344,680 tokens of word aligned Arabic and English parallel text enriched with linguistic tags. This material was used as training data in the DARPA GALE (Global Autonomous Language Exploitation) program. Some approaches to statistical machine translation include the incorporation of linguistic knowledge in word aligned text as a means to improve automatic word alignment and machine translation quality. This is accomplished with two annotation schemes: alignment and tagging. Alignment identifies minimum translation units and translation relations by using minimum-match and attachment annotation approaches. A set of word tags and alignment link tags are designed in the tagging scheme to describe these translation units and relations. Tagging adds contextual, syntactic and language-specific features to the alignment annotation. Other releases available in this series are: * GALE Chinese-English Word Alignment and Tagging Training Part 1 -- Newswire and Web (LDC2012T16) * GALE Chinese-English Word Alignment and Tagging Training Part 2 -- Newswire (LDC2012T20) * GALE Chinese-English Word Alignment and Tagging Training Part 3 -- Web (LDC2012T24) * GALE Chinese-English Word Alignment and Tagging Training Part 4 -- Web (LDC2013T05) * GALE Chinese-English Word Alignment and Tagging -- Broadcast Training Part 1 (LDC2013T23) *Data* This release consists of Arabic source newswire and web data collected by LDC in 2006 - 2008. The distribution by genre, words, character tokens and segments appears below: Language
Extent:Corpus size: 38824 KB
ISBN: 1-58563-671-1
ISLRN: 642-473-657-451-7
DOI: 10.35111/8pby-s456
Standard Arabic
Language (ISO639):eng
License:LDC User Agreement for Non-Members: https://catalog.ldc.upenn.edu/license/ldc-non-members-agreement.pdf
Medium:Distribution: Web Download
Publisher:Linguistic Data Consortium
Publisher (URI):https://www.ldc.upenn.edu
Relation (URI):https://catalog.ldc.upenn.edu/docs/LDC2014T05
Rights Holder: Portions © 2006, 2008 Agence France Presse, © 2006-2008 Al-Ahram, © 2006-2008 Al Hayat, © 2006-2008 Al-Quds Al-Arabi, © 2006-2008 An Nahar, © 2006-2008 Asharq Al-Awsat, © 2007 Assabah, © 2006 Xinhua News Agency, © 2006-2008, 2014 Trustees of the University of Pennsylvania
Type (DCMI):Text
Type (OLAC):primary_text


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Citation: Li, Xuansong; Grimes, Stephen; Ismael, Safa; Strassel, Stephanie. 2014. Linguistic Data Consortium.
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