Deep Learning for Arabic Error Detection and Correction
Research on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our rev...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2020
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://bspace.buid.ac.ae/handle/1234/2793 https://doi.org/10.1145/3373266. |
| الوسوم: |
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| _version_ | 1862980613878841344 |
|---|---|
| author | ALKHATIB, MANAR |
| author2 | ABDEL MONEM, AZZA SHAALAN, KHALED |
| author2_role | author author |
| author_facet | ALKHATIB, MANAR ABDEL MONEM, AZZA SHAALAN, KHALED |
| author_role | author |
| dc.creator.none.fl_str_mv | ALKHATIB, MANAR ABDEL MONEM, AZZA SHAALAN, KHALED |
| dc.date.none.fl_str_mv | 2020 2025-02-11T04:43:39Z 2025-02-11T04:43:39Z |
| dc.identifier.none.fl_str_mv | Alkhatib, M., Monem, A.A. and Shaalan, K. (2020) “Deep Learning for Arabic Error Detection and Correction,” ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 19(5), pp. 1–13. 2375-4699, 2375-4702 https://bspace.buid.ac.ae/handle/1234/2793 https://doi.org/10.1145/3373266. |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | ACM digital library |
| dc.relation.none.fl_str_mv | ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)v19 n5 (20200812): 1-13 |
| dc.subject.none.fl_str_mv | CCS Concepts: • Computing methodologies → Machine learning; Machine learning approaches; Neural networks; Additional Key Words and Phrases: Error detection, error correction, bidirectional long short-term memory, word embedding, polynomial network classifier |
| dc.title.none.fl_str_mv | Deep Learning for Arabic Error Detection and Correction |
| dc.type.none.fl_str_mv | Article |
| description | Research on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address the detection and correction of ill-formed words that do not conform to the Arabic morphology system. Even fewer systems address the detection and correction of erroneous well-formed Arabic words that are either contextually or semantically inconsistent within the text. We introduce an approach that investigates employing deep neural network technology for error detection in Arabic text. We have developed a systematic framework for spelling and grammar error detection, as well as correction at the word level, based on a bidirectional long short-term memory mechanism and word embedding, in which a polynomial network classifier is at the top of the sys tem. To get conclusive results, we have developed the most significant gold standard annotated corpus to date, containing 15 million fully inflected Arabic words. The data were collected from diverse text sources and genres, in which every erroneous and ill-formed word has been annotated, validated, and manually re vised by Arabic specialists. This valuable asset is available for the Arabic natural language processing research community. The experimental results confirm that our proposed system significantly outperforms the per formance of Microsoft Word 2013 and Open Office Ayaspell 3.4, which have been used in the literature for evaluating similar research. |
| id | budr_4d73d25a7ba04a74d78a68dad0517f89 |
| identifier_str_mv | Alkhatib, M., Monem, A.A. and Shaalan, K. (2020) “Deep Learning for Arabic Error Detection and Correction,” ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 19(5), pp. 1–13. 2375-4699, 2375-4702 |
| language_invalid_str_mv | en |
| network_acronym_str | budr |
| network_name_str | The British University in Dubai repository |
| oai_identifier_str | oai:bspace.buid.ac.ae:1234/2793 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | ACM digital library |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Deep Learning for Arabic Error Detection and CorrectionALKHATIB, MANARABDEL MONEM, AZZASHAALAN, KHALEDCCS Concepts: • Computing methodologies → Machine learning; Machine learning approaches; Neural networks; Additional Key Words and Phrases: Error detection, error correction, bidirectional long short-term memory, word embedding, polynomial network classifierResearch on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address the detection and correction of ill-formed words that do not conform to the Arabic morphology system. Even fewer systems address the detection and correction of erroneous well-formed Arabic words that are either contextually or semantically inconsistent within the text. We introduce an approach that investigates employing deep neural network technology for error detection in Arabic text. We have developed a systematic framework for spelling and grammar error detection, as well as correction at the word level, based on a bidirectional long short-term memory mechanism and word embedding, in which a polynomial network classifier is at the top of the sys tem. To get conclusive results, we have developed the most significant gold standard annotated corpus to date, containing 15 million fully inflected Arabic words. The data were collected from diverse text sources and genres, in which every erroneous and ill-formed word has been annotated, validated, and manually re vised by Arabic specialists. This valuable asset is available for the Arabic natural language processing research community. The experimental results confirm that our proposed system significantly outperforms the per formance of Microsoft Word 2013 and Open Office Ayaspell 3.4, which have been used in the literature for evaluating similar research.ACM digital library2025-02-11T04:43:39Z2025-02-11T04:43:39Z2020ArticleAlkhatib, M., Monem, A.A. and Shaalan, K. (2020) “Deep Learning for Arabic Error Detection and Correction,” ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), 19(5), pp. 1–13.2375-4699, 2375-4702https://bspace.buid.ac.ae/handle/1234/2793https://doi.org/10.1145/3373266.enACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)v19 n5 (20200812): 1-13oai:bspace.buid.ac.ae:1234/27932026-01-29T17:14:01Z |
| spellingShingle | Deep Learning for Arabic Error Detection and Correction ALKHATIB, MANAR CCS Concepts: • Computing methodologies → Machine learning; Machine learning approaches; Neural networks; Additional Key Words and Phrases: Error detection, error correction, bidirectional long short-term memory, word embedding, polynomial network classifier |
| title | Deep Learning for Arabic Error Detection and Correction |
| title_full | Deep Learning for Arabic Error Detection and Correction |
| title_fullStr | Deep Learning for Arabic Error Detection and Correction |
| title_full_unstemmed | Deep Learning for Arabic Error Detection and Correction |
| title_short | Deep Learning for Arabic Error Detection and Correction |
| title_sort | Deep Learning for Arabic Error Detection and Correction |
| topic | CCS Concepts: • Computing methodologies → Machine learning; Machine learning approaches; Neural networks; Additional Key Words and Phrases: Error detection, error correction, bidirectional long short-term memory, word embedding, polynomial network classifier |
| url | https://bspace.buid.ac.ae/handle/1234/2793 https://doi.org/10.1145/3373266. |