Arabic Gigaword
| Item Name: | Arabic Gigaword |
| Author(s): | David Graff |
| LDC Catalog No.: | LDC2003T12 |
| ISBN: | 1-58563-271-6 |
| ISLRN: | 537-362-711-928-4 |
| DOI: | https://doi.org/10.35111/ep1n-de95 |
| Release Date: | July 22, 2003 |
| Member Year(s): | 2003 |
| DCMI Type(s): | Text |
| Data Source(s): | newswire |
| Project(s): | EARS, GALE, TIDES |
| Application(s): | information retrieval, language modeling, natural language processing |
| Language(s): | Standard Arabic |
| Language ID(s): | arb |
| License(s): |
LDC User Agreement for Non-Members |
| Online Documentation: | LDC2003T12 Documents |
| Licensing Instructions: | Subscription & Standard Members, and Non-Members |
| Citation: | Graff, David. Arabic Gigaword LDC2003T12. Web Download. Philadelphia: Linguistic Data Consortium, 2003. |
| Related Works: | View |
Introduction
Arabic Gigaword was produced by the Linguistic Data Consortium (LDC) and contains approximately 1 million news documents totaling 400 million words of Arabic text. This is a comprehensive archive of newswire text data that has been acquired from Arabic news sources by LDC at the University of Pennsylvania.
Four distinct sources of Arabic newswire are represented here:
- Agence France Presse (AFA)
- Al Hayat News Agency (ALH)
- Al Nahar News Agency (ANN)
- Xinhua News Agency (XIN)
Much of the AFP content in this collection has been published previously by the LDC in Arabic Newswire Part 1 (LDC2001T55) and some of this content has also been included in an Arabic supplement to TDT3 (Topic Detection and Tracking) and as the Arabic component of TDT4. TDT4 also included a four-month sample from Al Hayat and An Nahar (October 2000 - January 2001). Apart from that, all of the Al Hayat, An Nahar, and Xinhua Arabic content, as well as AFP content for 2001-2002, is being released here for the first time.
Data
There are 319 files, totaling approximately 1.1 GB in compressed form, 4.3 GB uncompressed, and 391,619 K-words (thousands of words).
The table below presents the following categories of information: source of the data, number of files per source, and K-words (the number of space-separated tokens in the text, excluding SGML tags), and number of documents per source.
| Source | #Files | K-words | #DOCs |
|---|---|---|---|
| AFA | 104 | 94,484 | 516,855 |
| ALH | 95 | 139,501 | 305,250 |
| ANN | 96 | 140,247 | 327,768 |
| XIA | 24 | 17,387 | 106,846 |
| TOTAL | 319 | 391,619 | 1,256,719 |
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 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 corpus has been fully validated by a standard SGML parser utility (nsgmls), using the DTD file provided in the publication.
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," or "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. |
The general strategy for categorizing DOCs into these three classes was, for each source, to discover the most common and frequent clues in the text stream that correlated with the "non-story" types. When none of the known clues was in evidence, the DOC was classified as a "story."
Previous "Gigaword" corpora (in English and Chinese) had a fourth category, "advis" (for "advisory"), which applied to DOCs that contain text intended solely for news service editors, not the news-reading public. In preparing the Arabic data, the task of determining patterns for assigning "non-story" type labels was carried out by a native speaker of Arabic. For whatever reason, this person did not find the "advis" category to be applicable to any of the data.
Samples
For an example of the data in this corpus, please view this sample (TXT).
Updates
This edition of Arabic Gigaword has been superseded by a a new edtion, LDC2006T02