{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:21:15Z","timestamp":1753881675347,"version":"3.41.2"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"content-version":"vor","delay-in-days":97,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906158"],"award-info":[{"award-number":["61906158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Loanword identification is studied in recent years to alleviate data sparseness in several natural language processing (NLP) tasks, such as machine translation, cross\u2010lingual information retrieval, and so on. However, recent studies on this topic usually put efforts on high\u2010resource languages (such as Chinese, English, and Russian); for low\u2010resource languages, such as Uyghur and Mongolian, due to the limitation of resources and lack of annotated data, loanword identification on these languages tends to have lower performance. To overcome this problem, we first propose a lexical constraint\u2010based data augmentation method to generate training data for low\u2010resource language loanword identification; then, a loanword identification model based on a log\u2010linear RNN is introduced to improve the performance of low\u2010resource loanword identification by incorporating features such as word\u2010level embeddings, character\u2010level embeddings, pronunciation similarity, and part\u2010of\u2010speech (POS) into one model. Experimental results on loanword identification in Uyghur (in this study, we mainly focus on Arabic, Chinese, Russian, and Turkish loanwords in Uyghur) showed that our proposed method achieves best performance compared with several strong baseline systems.<\/jats:p>","DOI":"10.1155\/2021\/9975078","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T04:35:38Z","timestamp":1617942938000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Loanword Identification in Low\u2010Resource Language with Data Augmentation and Multiple Feature Fusion"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6367-6118","authenticated-orcid":false,"given":"Chenggang","family":"Mi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7948-4375","authenticated-orcid":false,"given":"Shaolin","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Nie","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"e_1_2_10_1_2","unstructured":"McCoyR. 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