Arabic Gigaword Second Edition
| Item Name: | Arabic Gigaword Second Edition |
| Author(s): | David Graff, Ke Chen, Junbo Kong, Kazuaki Maeda |
| LDC Catalog No.: | LDC2006T02 |
| ISBN: | 1-58563-371-2 |
| ISLRN: | 299-814-033-635-4 |
| DOI: | https://doi.org/10.35111/scbm-4q37 |
| Release Date: | January 19, 2006 |
| Member Year(s): | 2006 |
| DCMI Type(s): | Text |
| Data Source(s): | newswire |
| Application(s): | information retrieval, language modeling |
| Language(s): | Standard Arabic |
| Language ID(s): | arb |
| License(s): |
LDC User Agreement for Non-Members |
| Online Documentation: | LDC2006T02 Documents |
| Licensing Instructions: | Subscription & Standard Members, and Non-Members |
| Citation: | Graff, David, et al. Arabic Gigaword Second Edition LDC2006T02. Web Download. Philadelphia: Linguistic Data Consortium, 2006. |
| Related Works: | View |
Introduction
Arabic Gigaword Second Edition was developed by the Linguistic Data Consortium (LDC) and contains 1.6 million documents of Arabic newswire text collected by LDC.
This second edition includes all of the content of the first edition of Arabic Gigaword (LDC2003T12) as well as new data.
Data
The following table contains information for this corpus, broken down by source. The information includes source codes represented in the corpus as well as their codes from the first edition, the collection span and number of documents new to this edition, the number of documents total, and the K-words (thousands of words) for each source. Ummah Press is a new source included in the second edition and therefore has no first edition info.
| Source | Second Edition Codes | First Edition Codes | Second Edition Collection Span | New Docs | Total Docs | K-words |
| Agence France Presse | afp_arb | afa | 01/2003 - 12/2004 | 143,766 | 660,621 | 123,594 |
| Al Hayat New Agency | hyt_arb | alh | 01/2002 - 12/2003 | 64,308 | 369,555 | 169,100 |
| An Nahar News Agency | nhr_arb | ann | 01/2003 - 01/2004 | 16,316 | 344,084 | 151,078 |
| Ummah Press | umh_arb | 01/2003 - 12/2004 | 4,641 | 4,641 | 1,201 | |
| Xinhua News Agency | xin_arb | xia | 06/2003 - 12/2004 | 106,236 | 213,082 | 36,933 |
| Total | 335,267 | 1,591,983 | 481,906 |
Further statistics for each source are included in the corpus documentation. All text files in this corpus have been converted to UTF-8 character encoding.
Owing to the use of UTF-8, the SGML tagging within each file shows up as lines of single-byte-per-character (ASCII) text, whereas lines of actual text data, including article headlines and datelines, contain a mixture of single-byte and multi-byte characters. In general, single-byte characters in the text data will consist of digits and punctuation marks (where the original source relied on ASCII punctuation codes, rather than Arabic-specific punctuation), whereas multi-byte characters consist of Arabic letters and a small number of special punctuation or other symbols. This variable-width character encoding is intrinsic to UTF-8, and all UTF-8 capable processes will handle the data appropriately.
Each data file name consists of the seven-letter prefix, an underscore character ("_"), and a six-digit date representing the year and month during which the file contents were generated by the respective news source. Therefore, each file contains all the usable data received by LDC for the given month from the given news source.
All text data are presented in SGML form, using a very simple, minimal markup structure. The file gigaword_a.dtd in the "dtd" directory provides the formal "Document Type Declaration" for parsing the SGML content. The corpus has been fully validated by a standard SGML parser utility (nsgmls), using this DTD file.
Unlike older corpora, the present corpus uses only the information structure that is common to all sources and serves a clear function: headline, dateline, and core news content (usually containing paragraphs).
All sources have received a uniform treatment in terms of quality control, and have been categorized into three distinct "types":
| story | this type of DOC represents a coherent report on a particular topic or event, consisting of paragraphs and full sentences |
| multi | this type of DOC contains a series of unrelated "blurbs," each of which briefly describes a particular topic or event: "summaries of today's news," "news briefs in ... (some general area like finance or sports)" and so on |
| other | these DOCs clearly do not fall into any of the above types; these are things like lists of sports scores, stock prices, temperatures around the world, and so on |
Samples
For an example of the data in this corpus, please view this sample (TXT).