CANDLE applies CTC alignment to Arabic character deduplication, achieving 5.37% sentence error rate on clean text and up to 12.8% tokenizer fertility reduction.
1.5 billion words Arabic Corpus
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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.
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CANDLE: CTC-based Arabic Noisy-character Deduplication using a Lightweight Encoder
CANDLE applies CTC alignment to Arabic character deduplication, achieving 5.37% sentence error rate on clean text and up to 12.8% tokenizer fertility reduction.