Open AccessConstructing Arabic Language Resources from Google N-gram Dataset
Author(s) -
Imad Qasim Habeeb,
Zeyad Qasim Habeeb,
Yaseen Naser Jurn,
Hanan Najm Abdulkhudhur
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1530/1/012048
Subject(s) - n gram , computer science , arabic , lexicon , natural language processing , resource (disambiguation) , artificial intelligence , gram , information retrieval , language model , linguistics , computer network , philosophy , biology , bacteria , genetics
The Google N-gram dataset contains millions of books in 22 different categories. Therefore, it is used widely as a language resource in natural language processing. The Arabic language has several resources, but most of them are small in size. As with any research, if training resources are large, then the validity of any research is higher. To build a new language resource manually by humans take years to accomplish, but using automatic methods can be done in a few days. Google N-gram dataset does not support the Arabic language yet. This will cause a challenge for researchers who use this dataset for extracting Arabic text. In this paper, an extraction method that can produce a lexicon and N-gram corpus from the English Google N-gram dataset is presented. The steps to build these recourses and to overcome Google N-gram dataset challenges are explained. The experiment results show the success of the proposed method in the automatic construction of the resources in a short time. This method will be useful to researchers in several research fields.