GALE Arabic-English Parallel Aligned Treebank -- Broadcast News Part 2

Item Name: GALE Arabic-English Parallel Aligned Treebank -- Broadcast News Part 2
Author(s): Xuansong Li, Stephen Grimes, Safa Ismael, Stephanie Strassel, Mohamed Maamouri, Ann Bies
LDC Catalog No.: LDC2014T03
ISBN: 1-58563-666-5
ISLRN: 381-030-411-602-1
Release Date: February 17, 2014
Member Year(s): 2014
DCMI Type(s): Text
Data Source(s): broadcast news
Project(s): GALE
Application(s): machine translation, information detection, cross-lingual information retrieval, automatic content extraction
Language(s): English, Standard Arabic, Arabic
Language ID(s): eng, arb, ara
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2014T03 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Li, Xuansong, et al. GALE Arabic-English Parallel Aligned Treebank -- Broadcast News Part 2 LDC2014T03. Web Download. Philadelphia: Linguistic Data Consortium, 2014.

Introduction

GALE Arabic-English Parallel Aligned Treebank -- Broadcast News Part 2 was developed by the Linguistic Data Consortium (LDC) and contains 141,058 tokens of word aligned Arabic and English parallel text with treebank annotations. This material was used as training data in the DARPA GALE (Global Autonomous Language Exploitation) program.

Parallel aligned treebanks are treebanks annotated with morphological and syntactic structures aligned at the sentence level and the sub-sentence level. Such data sets are useful for natural language processing and related fields, including automatic word alignment system training and evaluation, transfer-rule extraction, word sense disambiguation, translation lexicon extraction and cultural heritage and cross-linguistic studies. With respect to machine translation system development, parallel aligned treebanks may improve system performance with enhanced syntactic parsers, better rules and knowledge about language pairs and reduced word error rate.

The source Arabic data was translated into English. Arabic and English treebank annotations were performed independently. The parallel texts were then word aligned. The material in this corpus corresponds to a portion of the Arabic treebanked data in Arabic Treebank - Broadcast News v1.0 (LDC2012T07).

LDC previously released GALE Arabic-English Parallel Aligned Treebank -- Broadcast News Part 1 (LDC2013T14).

Data

The source data consists of Arabic broadcast news programming collected by LDC in 2007 and 2008 from Al Arabiya, Abu Dhabi TV, Al Baghdadya TV, Al Fayha, Alhurra, Al Iraqiyah, Aljazeera, Al Ordiniyah, Al Sharqiya, Dubai TV, Oman TV, Radio Sawa and Saudi TV. All data is encoded as UTF-8. A count of files, words, tokens and segments is below.

Language</TD> Files Words Tokens Segments
Arabic 31 110,690 141,058 7,102

Note: Word count is based on the untokenized Arabic source. Token count is based on the ATB-tokenized Arabic source.

The purpose of the GALE word alignment task was to find correspondences between words, phrases or groups of words in a set of parallel texts. Arabic-English word alignment annotation consisted of the following tasks:

  • Identifying different types of links: translated (correct or incorrect) and not translated (correct or incorrect)
  • Identifying sentence segments not suitable for annotation, e.g., blank segments, incorrectly-segmented segments, segments with foreign languages
  • Tagging unmatched words attached to other words or phrases

This release contains four types of files - raw, tokenized, treebank, and wa. The raw format contains the original Arabic and English sentences without any annotation. The tokenized format is the treebank tokenized version of the raw data which may contain Empty Category tokens (treebank leaves that have the POS label -NONE-). The treebank and wa files are treebank and word alignment annotations on the tokenized files.

Samples

Please view the following samples

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.

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