Pith. sign in

REVIEW 2 cited by

1.5 billion words Arabic Corpus

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1611.04033 v1 pith:RKMBNGVW submitted 2016-11-12 cs.CL cs.DLcs.IR

Ibrahim Abu El-khair This is my paper · ORCID
classification cs.CLcs.DLcs.IR
keywords corpusarabicwordsarticlesbillionmillionnamelynewspaper
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This study is an attempt to build a contemporary linguistic corpus for Arabic language. The corpus produced, is a text corpus includes more than five million newspaper articles. It contains over a billion and a half words in total, out of which, there is about three million unique words. The data were collected from newspaper articles in ten major news sources from eight Arabic countries, over a period of fourteen years. The corpus was encoded with two types of encoding, namely: UTF-8, and Windows CP-1256. Also it was marked with two mark-up languages, namely: SGML, and XML.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CANDLE: CTC-based Arabic Noisy-character Deduplication using a Lightweight Encoder

    cs.CL 2026-06 unverdicted novelty 6.0 of 10

    CANDLE uses CTC on lightweight character encoders for Arabic noise deduplication, reporting 5.37% SER on benchmarks and up to 12.8% tokenizer fertility reduction.

  2. Towards the Development of Balanced Synthetic Data for Correcting Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and Synthetic Data Generating Model

    cs.CL 2025-02 reject novelty 6.0 of 10

    A tag-conditioned Arabic synthetic data pipeline is claimed to set a new GEC state of the art, but the reported 79.36% F1 is the F0.5 score from the paper's own table.

Pith tools