Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields
Arabic diacritics play a significant role in distinguishing words with the same orthography but different meanings, pronunciations, and syntactic functions. The presence of Arabic diacritics can be useful in many natural language processing applications, such as text-to-speech tasks, machine transla...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9174712/ |
| _version_ | 1818579346432983040 |
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| author | Abdulmohsen Al-Thubaity Atheer Alkhalifa Abdulrahman Almuhareb Waleed Alsanie |
| author_facet | Abdulmohsen Al-Thubaity Atheer Alkhalifa Abdulrahman Almuhareb Waleed Alsanie |
| author_sort | Abdulmohsen Al-Thubaity |
| collection | DOAJ |
| description | Arabic diacritics play a significant role in distinguishing words with the same orthography but different meanings, pronunciations, and syntactic functions. The presence of Arabic diacritics can be useful in many natural language processing applications, such as text-to-speech tasks, machine translation, and part-of-speech tagging. This article discusses the use of bidirectional long short-term memory neural networks with conditional random fields for Arabic diacritization. This approach requires no morphological analyzers, dictionary, or feature engineering, but rather uses a sequence-to-sequence schema. The input is a sequence of characters that constitute the sentence, and the output consists of the corresponding diacritic(s) for each character in that sentence. The performance of the proposed approach was examined using four datasets with different sizes and genres, namely, the King Abdulaziz City for Science and Technology text-to-speech (KACST TTS) dataset, the Holy Quran, Sahih Al-Bukhary, and the Penn Arabic Treebank (ATB). For training, 60% of the sentences were randomly selected from each dataset, 20% were selected for validation, and 20% were selected for testing. The trained models achieved diacritic error rates of 3.41%, 1.34%, 1.57%, and 2.13% and word error rates of 14.46%, 4.92%, 5.65%, and 8.43% on the KACST TTS, Holy Quran, Sahih Al-Bukhary, and ATB datasets, respectively. Comparison of the proposed method with those used in other studies and existing systems revealed that its results are comparable to or better than those of the state-of-the-art methods. |
| first_indexed | 2024-12-16T07:00:15Z |
| format | Article |
| id | doaj.art-45a642c1dfb3491e94bf8a6701c801e2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T07:00:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-45a642c1dfb3491e94bf8a6701c801e22022-12-21T22:40:10ZengIEEEIEEE Access2169-35362020-01-01815498415499610.1109/ACCESS.2020.30188859174712Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random FieldsAbdulmohsen Al-Thubaity0https://orcid.org/0000-0003-2376-0849Atheer Alkhalifa1https://orcid.org/0000-0002-6576-0735Abdulrahman Almuhareb2https://orcid.org/0000-0001-5053-6530Waleed Alsanie3https://orcid.org/0000-0002-8525-4645National Center for Data Analytics and Artificial Intelligence, KACST, Riyadh, Saudi ArabiaNational Center for Data Analytics and Artificial Intelligence, KACST, Riyadh, Saudi ArabiaNational Center for Data Analytics and Artificial Intelligence, KACST, Riyadh, Saudi ArabiaNational Center for Data Analytics and Artificial Intelligence, KACST, Riyadh, Saudi ArabiaArabic diacritics play a significant role in distinguishing words with the same orthography but different meanings, pronunciations, and syntactic functions. The presence of Arabic diacritics can be useful in many natural language processing applications, such as text-to-speech tasks, machine translation, and part-of-speech tagging. This article discusses the use of bidirectional long short-term memory neural networks with conditional random fields for Arabic diacritization. This approach requires no morphological analyzers, dictionary, or feature engineering, but rather uses a sequence-to-sequence schema. The input is a sequence of characters that constitute the sentence, and the output consists of the corresponding diacritic(s) for each character in that sentence. The performance of the proposed approach was examined using four datasets with different sizes and genres, namely, the King Abdulaziz City for Science and Technology text-to-speech (KACST TTS) dataset, the Holy Quran, Sahih Al-Bukhary, and the Penn Arabic Treebank (ATB). For training, 60% of the sentences were randomly selected from each dataset, 20% were selected for validation, and 20% were selected for testing. The trained models achieved diacritic error rates of 3.41%, 1.34%, 1.57%, and 2.13% and word error rates of 14.46%, 4.92%, 5.65%, and 8.43% on the KACST TTS, Holy Quran, Sahih Al-Bukhary, and ATB datasets, respectively. Comparison of the proposed method with those used in other studies and existing systems revealed that its results are comparable to or better than those of the state-of-the-art methods.https://ieeexplore.ieee.org/document/9174712/Arabic diacritic restorationbi-directional long short-term memorycomputational linguisticsconditional random fieldsdeep learningneural network |
| spellingShingle | Abdulmohsen Al-Thubaity Atheer Alkhalifa Abdulrahman Almuhareb Waleed Alsanie Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields IEEE Access Arabic diacritic restoration bi-directional long short-term memory computational linguistics conditional random fields deep learning neural network |
| title | Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields |
| title_full | Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields |
| title_fullStr | Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields |
| title_full_unstemmed | Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields |
| title_short | Arabic Diacritization Using Bidirectional Long Short-Term Memory Neural Networks With Conditional Random Fields |
| title_sort | arabic diacritization using bidirectional long short term memory neural networks with conditional random fields |
| topic | Arabic diacritic restoration bi-directional long short-term memory computational linguistics conditional random fields deep learning neural network |
| url | https://ieeexplore.ieee.org/document/9174712/ |
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