{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:10:14Z","timestamp":1780675814753,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Strengthening Plan of National Defense Science and Technology Foundation of China","award":["2021-JCJQ-JJ-0059"],"award-info":[{"award-number":["2021-JCJQ-JJ-0059"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2003207"],"award-info":[{"award-number":["U2003207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/info13040175","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T21:34:29Z","timestamp":1648762469000},"page":"175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Chinese-Uyghur Bilingual Lexicon Extraction Based on Weak Supervision"],"prefix":"10.3390","volume":"13","author":[{"given":"Anwar","family":"Aysa","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mijit","family":"Ablimit","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hankiz","family":"Yilahun","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-308X","authenticated-orcid":false,"given":"Askar","family":"Hamdulla","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ballesteros, L.A. (2002). Cross-language retrieval via transitive translation. Advances in Information Retrieval, Springer.","DOI":"10.1007\/0-306-47019-5_8"},{"key":"ref_2","unstructured":"Zou, W.Y., Socher, R., Cer, D., and Manning, C.D. (2013, January 18\u201321). Bilingual word embeddings for phrase-based machine translation. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, WA, USA."},{"key":"ref_3","unstructured":"Klementiev, A., Titov, I., and Bhattarai, B. (2012, January 8\u201315). Inducing crosslingual distributed representations of words. Proceedings of the COLING 2012, Mumbai, India."},{"key":"ref_4","unstructured":"Zhang, M., Liu, Y., Luan, H., and Sun, M. (August, January 30). Adversarial training for unsupervised bilingual lexicon induction. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, BC, Canada."},{"key":"ref_5","unstructured":"Lauly, S., Larochelle, H., Khapra, M.M., Ravindran, B., Raykar, V., and Saha, A. (2014). An autoencoder approach to learning bilingual word representations. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nassirudin, M., and Purwarianti, A. (2015, January 10\u201311). Indonesian-Japanese term extraction from bilingual corpora using machine learning. Proceedings of the 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia.","DOI":"10.1109\/ICACSIS.2015.7415180"},{"key":"ref_7","first-page":"89","article-title":"Neural Network Technology Application and Progress for the Field of Medicine","volume":"34","author":"Liang","year":"2011","journal-title":"J. Liaoning Univ. Tradit. Chin. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1613\/jair.1.11640","article-title":"A survey of cross-lingual word embedding models","volume":"65","author":"Ruder","year":"2019","journal-title":"J. Artif. Intell. Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rapp, R. (1999, January 20\u201326). Automatic identification of word translations from unrelated English and German corpora. Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, College Park, MD, USA.","DOI":"10.3115\/1034678.1034756"},{"key":"ref_10","first-page":"33","article-title":"Automatic extraction of bilingual term lexicon from parallel corpora","volume":"14","author":"Sun","year":"2000","journal-title":"J. Chin. Inf. Process."},{"key":"ref_11","first-page":"78","article-title":"A bilingual word alignment method of Vietnamese-Chinese based on deep neutral network","volume":"51","author":"Mo","year":"2016","journal-title":"J. Shandong Univ. Nat. Sci."},{"key":"ref_12","unstructured":"Luong, M.-T., Pham, H., and Manning, C.D. (June, January 31). Bilingual word representations with monolingual quality in mind. Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, Denver, CO, USA."},{"key":"ref_13","unstructured":"Morin, E., and Prochasson, E. (2011, January 24). Bilingual lexicon extraction from comparable corpora enhanced with parallel corpora. Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web, Portland, OR, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gouws, S., and S\u00f8gaard, A. (June, January 31). Simple task-specific bilingual word embeddings. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, CO, USA.","DOI":"10.3115\/v1\/N15-1157"},{"key":"ref_15","unstructured":"Mikolov, T., Le, Q.V., and Sutskever, I. (2013). Exploiting similarities among languages for machine translation. arXiv."},{"key":"ref_16","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","volume":"26","author":"Mikolov","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wick, M., Kanani, P., and Pocock, A. (2016, January 12\u201317). Minimally-constrained multilingual embeddings via artificial code-switching. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10360"},{"key":"ref_18","unstructured":"Conneau, A., Lample, G., Ranzato, M.A., Denoyer, L., and J\u00e9gou, H. (2017). Word translation without parallel data. arXiv."},{"key":"ref_19","unstructured":"Barone, A.V.M. (2016). Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders. arXiv."},{"key":"ref_20","unstructured":"Cao, H., Zhao, T., Zhang, S., and Meng, Y. (2016, January 11\u201317). A distribution-based model to learn bilingual word embeddings. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan."},{"key":"ref_21","first-page":"528","article-title":"Research on bilingual term extraction based on Chinese Uygur medical parallel corpus","volume":"49","author":"Yu","year":"2018","journal-title":"J. Inn. Mong. Univ."},{"key":"ref_22","unstructured":"Silva, V.S., Freitas, A., and Handschuh, S. (2020). Xte: Explainable text entailment. arXiv."},{"key":"ref_23","first-page":"1137","article-title":"A neural probabilistic language model","volume":"3","author":"Bengio","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mnih, A., and Hinton, G. (2007, January 20\u201324). Three new graphical models for statistical language modelling. Proceedings of the 24th International Conference on Machine learning, Corvallis, OR, USA.","DOI":"10.1145\/1273496.1273577"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lai, S., Xu, L., Liu, K., and Zhao, J. (2015, January 25\u201330). Recurrent convolutional neural networks for text classification. Proceedings of the Twenty-ninth AAAI Conference on Artificial Intelligence, Austin, TX, USA.","DOI":"10.1609\/aaai.v29i1.9513"},{"key":"ref_26","unstructured":"Goldberg, Y., and Levy, O. (2014). word2vec Explained: Deriving Mikolov et al.\u2019s negative-sampling word-embedding method. arXiv."},{"key":"ref_27","unstructured":"Chen, Y.Q., Nixon, M.S., and Damper, R.I. (December, January 27). Implementing the k-nearest neighbour rule via a neural network. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1162\/tacl_a_00134","article-title":"Improving distributional similarity with lessons learned from word embeddings","volume":"3","author":"Levy","year":"2015","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_29","first-page":"1","article-title":"Learning Bilingual Word Embedding Mappings with Similar Words in Related Languages Using GAN","volume":"10","author":"Alipour","year":"2022","journal-title":"Appl. Artif. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s13278-020-00658-3","article-title":"Feature selection methods for event detection in Twitter: A text mining approach","volume":"10","author":"Hossny","year":"2020","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_31","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Artetxe, M., Labaka, G., and Agirre, E. (2019). Bilingual lexicon induction through unsupervised machine translation. arXiv.","DOI":"10.18653\/v1\/P19-1494"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shigeto, Y., Suzuki, I., Hara, K., Shimbo, M., and Matsumoto, Y. (2015, January 7\u201311). Ridge regression, hubness, and zero-shot learning. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Porto, Portugal.","DOI":"10.1007\/978-3-319-23528-8_9"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Joulin, A., Bojanowski, P., Mikolov, T., J\u00e9gou, H., and Grave, E. (2018). Loss in translation: Learning bilingual word mapping with a retrieval criterion. arXiv.","DOI":"10.18653\/v1\/D18-1330"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, M., Xu, K., Kawarabayashi, K.-I., Jegelka, S., and Boyd-Graber, J. (2019). Are Girls Neko or Sh\\= ojo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization. arXiv.","DOI":"10.18653\/v1\/P19-1307"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/4\/175\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:47:35Z","timestamp":1760136455000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/4\/175"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,31]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["info13040175"],"URL":"https:\/\/doi.org\/10.3390\/info13040175","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,31]]}}}