Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision
Bilingual lexicon extraction is useful, especially for low-resource languages that can leverage from high-resource languages. The Uyghur language is a derivative language, and its language resources are scarce and noisy. Moreover, it is difficult to find a bilingual resource to utilize the linguisti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/13/4/175 |
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| author | Anwar Aysa Mijit Ablimit Hankiz Yilahun Askar Hamdulla |
| author_facet | Anwar Aysa Mijit Ablimit Hankiz Yilahun Askar Hamdulla |
| author_sort | Anwar Aysa |
| collection | DOAJ |
| description | Bilingual lexicon extraction is useful, especially for low-resource languages that can leverage from high-resource languages. The Uyghur language is a derivative language, and its language resources are scarce and noisy. Moreover, it is difficult to find a bilingual resource to utilize the linguistic knowledge of other large resource languages, such as Chinese or English. There is little related research on unsupervised extraction for the Chinese-Uyghur languages, and the existing methods mainly focus on term extraction methods based on translated parallel corpora. Accordingly, unsupervised knowledge extraction methods are effective, especially for the low-resource languages. This paper proposes a method to extract a Chinese-Uyghur bilingual dictionary by combining the inter-word relationship matrix mapped by the neural network cross-language word embedding vector. A seed dictionary is used as a weak supervision signal. A small Chinese-Uyghur parallel data resource is used to map the multilingual word vectors into a unified vector space. As the word-particles of these two languages are not well-coordinated, stems are used as the main linguistic particles. The strong inter-word semantic relationship of word vectors is used to associate Chinese-Uyghur semantic information. Two retrieval indicators, such as nearest neighbor retrieval and cross-domain similarity local scaling, are used to calculate similarity to extract bilingual dictionaries. The experimental results show that the accuracy of the Chinese-Uyghur bilingual dictionary extraction method proposed in this paper is improved to 65.06%. This method helps to improve Chinese-Uyghur machine translation, automatic knowledge extraction, and multilingual translations. |
| first_indexed | 2024-03-09T10:34:25Z |
| format | Article |
| id | doaj.art-ad3318c94593407fbc16463e524c0de7 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-09T10:34:25Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-ad3318c94593407fbc16463e524c0de72023-12-01T21:05:15ZengMDPI AGInformation2078-24892022-03-0113417510.3390/info13040175Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak SupervisionAnwar Aysa0Mijit Ablimit1Hankiz Yilahun2Askar Hamdulla3College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaBilingual lexicon extraction is useful, especially for low-resource languages that can leverage from high-resource languages. The Uyghur language is a derivative language, and its language resources are scarce and noisy. Moreover, it is difficult to find a bilingual resource to utilize the linguistic knowledge of other large resource languages, such as Chinese or English. There is little related research on unsupervised extraction for the Chinese-Uyghur languages, and the existing methods mainly focus on term extraction methods based on translated parallel corpora. Accordingly, unsupervised knowledge extraction methods are effective, especially for the low-resource languages. This paper proposes a method to extract a Chinese-Uyghur bilingual dictionary by combining the inter-word relationship matrix mapped by the neural network cross-language word embedding vector. A seed dictionary is used as a weak supervision signal. A small Chinese-Uyghur parallel data resource is used to map the multilingual word vectors into a unified vector space. As the word-particles of these two languages are not well-coordinated, stems are used as the main linguistic particles. The strong inter-word semantic relationship of word vectors is used to associate Chinese-Uyghur semantic information. Two retrieval indicators, such as nearest neighbor retrieval and cross-domain similarity local scaling, are used to calculate similarity to extract bilingual dictionaries. The experimental results show that the accuracy of the Chinese-Uyghur bilingual dictionary extraction method proposed in this paper is improved to 65.06%. This method helps to improve Chinese-Uyghur machine translation, automatic knowledge extraction, and multilingual translations.https://www.mdpi.com/2078-2489/13/4/175bilingual dictionaryseed dictionarycross-language word embedding |
| spellingShingle | Anwar Aysa Mijit Ablimit Hankiz Yilahun Askar Hamdulla Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision Information bilingual dictionary seed dictionary cross-language word embedding |
| title | Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision |
| title_full | Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision |
| title_fullStr | Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision |
| title_full_unstemmed | Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision |
| title_short | Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision |
| title_sort | chinese uyghur bilingual lexicon extraction based on weak supervision |
| topic | bilingual dictionary seed dictionary cross-language word embedding |
| url | https://www.mdpi.com/2078-2489/13/4/175 |
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