{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T17:30:38Z","timestamp":1762018238580,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T00:00:00Z","timestamp":1578268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Opening Foundation of the Key Laboratory of Xinjiang Uyghur Autonomous Region of China","award":["2018D04019"],"award-info":[{"award-number":["2018D04019"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61762084, 61662077, and 61462083"],"award-info":[{"award-number":["61762084, 61662077, and 61462083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Scientific Research Program of the State Language Commission of China","award":["ZDI135-54"],"award-info":[{"award-number":["ZDI135-54"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur.<\/jats:p>","DOI":"10.3390\/info11010031","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T10:34:46Z","timestamp":1578306886000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3882-8981","authenticated-orcid":false,"given":"Ayiguli","family":"Halike","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, Xinjiang, China"},{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, Xinjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kahaerjiang","family":"Abiderexiti","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, Xinjiang, China"},{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, Xinjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuergen","family":"Yibulayin","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830046, Xinjiang, China"},{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, Xinjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,6]]},"reference":[{"key":"ref_1","unstructured":"Nogueira, C., Santos, D., Xiang, B., and Zhou, B. (2015, January 26\u201331). Classifying relations by ranking with convolutional neural networks. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Han, X., Liu, Z., and Sun, M. (2018, January 2\u20137). Neural knowledge acquisition via mutual attention between knowledge graph and text. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11927"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zeng, X., Zeng, D., and He, S. (2018, January 15\u201320). Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1047"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Luan, Y., He, L., Ostendorf, M., and Hajishirzi, H. (November, January 31). Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1360"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fader, A., Zettlemoyer, L., and Etzioni, O. (2014, January 24\u201327). Open question answering over curated and extracted knowledge bases. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, San Diego, CA, USA.","DOI":"10.1145\/2623330.2623677"},{"key":"ref_6","first-page":"505","article-title":"Answer selection in community question answering via attentive neural networks","volume":"24","author":"Xiang","year":"2017","journal-title":"IEEE SPL"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"167","DOI":"10.3233\/SW-140134","article-title":"DBpedia\u2014A large-scale, multilingual knowledge base extracted from Wikipedia","volume":"6","author":"Lehmann","year":"2015","journal-title":"Semant. Web J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhou, P., Shi, W., and Tian, J. (2016, January 7\u201312). Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-2034"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xu, K., Feng, Y., and Huang, S. (2015, January 17\u201321). Sematic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Proceeding, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1062"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zeng, D., and Liu, K. (2015, January 17\u201321). Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Proceeding, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1203"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, Y., Shen, S., and Liu, Z. (2016, January 7\u201312). Neural Relation Extraction with Selective Attention over Instances. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-1200"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, Y., and Mou, L.I. (2015, January 17\u201321). Classifying Relation via Long Short Term Memory Networks along Shortest Dependency Paths. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Proceeding, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1206"},{"key":"ref_13","first-page":"247","article-title":"Knowledge representation learning: A review","volume":"53","author":"Liu","year":"2016","journal-title":"J. Comput. Res. Dev."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Abiderexiti, K., Maimaiti, M., Yibulayin, T., and Wumaier, A. (2016, January 21\u201323). Annotation schemes for constructing Uyghur named entity relation corpus. Proceedings of the 2016 International Conference on Asian Language Processing (IALP), Tainan, Taiwan.","DOI":"10.1109\/IALP.2016.7875945"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Parhat, S., Ablimit, M., and Hamdulla, A. (2019). A Robust Morpheme Sequence and Convolutional Neural Network-Based Uyghur and Kazakh Short Text Classification. Information, 10.","DOI":"10.3390\/info10120387"},{"key":"ref_16","unstructured":"Takanobu, R., Zhang, T., and Liu, J. (2018). A Hierarchical Framework for Relation Extraction with Reinforcement Learning. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., and Verspoor, K. (2019, January 14\u201318). End-to-End Neural Relation Extraction Using Deep Biaffine Attention. Proceedings of the 41st European Conference on Information Retrieval (ECIR 2019), Cologne, Germany.","DOI":"10.1007\/978-3-030-15712-8_47"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016, January 12\u201317). Neural architectures for named entity recognition. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, Q., and Ji, H. (2014, January 23\u201325). Incremental joint extraction of entity mentions and relations. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD, USA.","DOI":"10.3115\/v1\/P14-1038"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Miwa, M., and Bansal, M. (2016, January 7\u201312). End-to-end relation extraction using LSTMs on sequences and tree structures. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-1105"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, Z., and Shen, C. (2019). Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0736-9"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ren, X., Wu, Z., He, W., Qu, M., Voss, C.R., Ji, H., Abdelzaher, T.F., and Han, J. (2017, January 3\u20137). Cotype: Joint extraction of typed entities and relations with knowledge bases. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia.","DOI":"10.1145\/3038912.3052708"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, Z., Xiong, C., and Sun, M. (2018, January 15\u201320). Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1223"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, P., and Barbosa, D. (2019, January 3\u20135). Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, MN, USA.","DOI":"10.18653\/v1\/N19-1323"},{"key":"ref_25","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"Collobert","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","unstructured":"Zhang, Z., Han, X., and Liu, Z. (August, January 28). ERNIE: Enhanced Language Representation with Informative Entities. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Fortezza da Basso, Florence, Italy."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.-R., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yao, K., Peng, B., Zhang, Y., Yu, D., Zweig, G., and Shi, Y. (2014, January 7\u201310). Spoken language understanding using long short-term memory neural networks. Proceedings of the 2014 IEEE Spoken Language Technology Workshop (SLT), South Lake Tahoe, NV, USA.","DOI":"10.1109\/SLT.2014.7078572"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yao, K., Peng, B., Zweig, G., Yu, D., Li, X., and Gao, F. (2014, January 4\u20139). Recurrent conditional random field for language understanding. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854368"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mintz, M., Bills, S., Snow, R., and Jurafsky, D. (2009, January 2\u20137). Distant supervision for relation extraction without labeled data. Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2\u2014ACL-IJCNLP \u201809, Suntec, Singapore.","DOI":"10.3115\/1690219.1690287"},{"key":"ref_31","unstructured":"Li, Y., Jiang, J., Chieu, H.L., and Chai, K.M.A. (2011, January 8\u201313). Extracting relation descriptors with conditional random fields. Proceedings of the 5th International Joint Conference on Natural Language Processing, Chiang Mai, Thailand."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Quan, C., Hua, L., Sun, X., and Bai, W. (2016). Multichannel convolutional neural network for biological relation extraction. Biomed Res. Int.","DOI":"10.1155\/2016\/1850404"},{"key":"ref_33","unstructured":"Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., and Xu, B. (August, January 30). Joint extraction of entities and relations based on a novel tagging scheme. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, BC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, Y., and Fu, G. (2017, January 7\u201311). End-to-end neural relation extraction with global optimization. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark.","DOI":"10.18653\/v1\/D17-1182"},{"key":"ref_35","first-page":"1381","article-title":"An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition","volume":"8","author":"Luo","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Murakami, Y., and Lin, D. (2016). Building contemporary Uyghur grammatical information dictionary. Worldwide Language Service Infrastructure. WLSI 2015, Springer.","DOI":"10.1007\/978-3-319-31468-6"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Murakami, Y., and Lin, D. (2016). Building Uyghur dependency treebank: Design principles, annotation schema and tools. Worldwide Language Service Infrastructure. WLSI 2015. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-319-31468-6"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Maimaiti, M., Wumaier, A., Abiderexiti, K., and Yibulayin, T. (2017). Bidirectional long short-term memory network with a conditional random field layer for Uyghur part-of-speech tagging. Information, 8.","DOI":"10.3390\/info8040157"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neucom.2016.12.075","article-title":"Joint entity and relation extraction based on a hybrid neural network","volume":"257","author":"Zheng","year":"2017","journal-title":"Neurocomputing"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/1\/31\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:28:52Z","timestamp":1760362132000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/1\/31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,6]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["info11010031"],"URL":"https:\/\/doi.org\/10.3390\/info11010031","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2020,1,6]]}}}