{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:48:51Z","timestamp":1774352931090,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,5]],"date-time":"2020-01-05T00:00:00Z","timestamp":1578182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Traditional methods for identifying naming ignore the correlation between named entities and lose hierarchical structural information between the named entities in a given text. Although traditional named-entity methods are effective for conventional datasets that have simple structures, they are not as effective for sports texts. This paper proposes a Chinese sports text named-entity recognition method based on a character graph convolutional neural network (Char GCN) with a self-attention mechanism model. In this method, each Chinese character in the sports text is regarded as a node. The edge between the nodes is constructed using a similar character position and the character feature of the named-entity in the sports text. The internal structural information of the entity is extracted using a character map convolutional neural network. The hierarchical semantic information of the sports text is captured by the self-attention model to enhance the relationship between the named entities and capture the relevance and dependency between the characters. The conditional random fields classification function can accurately identify the named entities in the Chinese sports text. The results conducted on four datasets demonstrate that the proposed method improves the F-Score values significantly to 92.51%, 91.91%, 93.98%, and 95.01%, respectively, in comparison to the traditional naming methods.<\/jats:p>","DOI":"10.3390\/info11010030","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T03:48:48Z","timestamp":1578282528000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Xieraili","family":"Seti","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"},{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aishan","family":"Wumaier","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"},{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Turgen","family":"Yibulayin","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"},{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diliyaer","family":"Paerhati","sequence":"additional","affiliation":[{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lulu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"},{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alimu","family":"Saimaiti","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"},{"name":"Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xie, J., Yang, Z., Neubig, G., Smith, N.A., and Carbonell, J. (November, January 31). Neural cross-lingual named entity recognition with minimal resources. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1034"},{"key":"ref_2","first-page":"163","article-title":"Research on named entity recognition in sports events field","volume":"43","author":"Gu","year":"2018","journal-title":"J. Henan Norm. Univ. Nat. Sci. Ed."},{"key":"ref_3","first-page":"872","article-title":"Named entity recognition method based on BLSTM","volume":"20","author":"Feng","year":"2018","journal-title":"Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"i37","DOI":"10.1093\/bioinformatics\/btx228","article-title":"Deep learning with word embeddings improves biomedical named entity recognition","volume":"33","author":"Habibi","year":"2017","journal-title":"Bioinformatics"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pham, T.H., and Le-Hong, P. (2017). End-to-end recurrent neural network models for Vietnamese named entity recognition: Word-level vs. character-level. Communications in Computer and Information Science, Proceedings of the International Conference of the Pacific Association for Computational Linguistics, Yangon, Myanmar, 16\u201318 August 2017, Springer.","DOI":"10.1007\/978-981-10-8438-6_18"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.csl.2017.01.012","article-title":"Generalisation in named entity recognition: A quantitative analysis","volume":"44","author":"Augenstein","year":"2017","journal-title":"Comput. Speech Lang."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.jbi.2017.11.007","article-title":"Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition","volume":"76","author":"Unanue","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"ref_8","unstructured":"Lee, J.Y., Dernoncourt, F., and Szolovits, P. (2017). Transfer learning for named-entity recognition with neural networks. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, C., Chen, W., and Xu, B. (2017, January 13\u201315). Named entity recognition with gated convolutional neural networks. Proceedings of the International Symposium on Natural Language Processing Based on Naturally Annotated Big Data China National Conference on Chinese Computational Linguistics, Nanjing, China.","DOI":"10.1007\/978-3-319-69005-6_10"},{"key":"ref_10","first-page":"1381","article-title":"An attention-based approach for chemical compound and drug named entity recognition","volume":"34","author":"Luo","year":"2018","journal-title":"J. Comput. Res. Dev."},{"key":"ref_11","unstructured":"Cetoli, A., Bragaglia, S., O\u2019Harney, A.D., and Sloan, M. (2017). Graph convolutional networks for named entity recognition. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Song, Y., Shi, S., and Li, J. (2018, January 13\u201319). Joint learning embeddings for Chinese words and their components via ladder structured networks. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/608"},{"key":"ref_13","unstructured":"Huang, W., and Wang, J. (2016). Character-level convolutional network for text classification applied to Chinese corpus. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1162\/tacl_a_00104","article-title":"Named entity recognition with bidirectional LSTM-CNNs","volume":"4","author":"Chiu","year":"2016","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"145301","DOI":"10.1103\/PhysRevLett.120.145301","article-title":"Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties","volume":"120","author":"Xie","year":"2018","journal-title":"Phys. Rev. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1039\/C8SC04228D","article-title":"A graph-convolutional neural network model for the prediction of chemical reactivity","volume":"10","author":"Coley","year":"2019","journal-title":"Chem. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.commatsci.2019.02.046","article-title":"Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks","volume":"162","author":"Schwarzer","year":"2019","journal-title":"Comput. Mater. Sci."},{"key":"ref_18","unstructured":"Abu-El-Haija, S., Kapoor, A., Perozzi, B., and Lee, J. (2018). N-GCN: Multi-scale graph convolution for semi-supervised node classification. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Abdelpakey, M.H., Shehata, M.S., and Mohamed, M.M. (2018, January 19\u201321). Denssiam: End-to-end densely-Siamese network with self-attention model for object tracking. Proceedings of the International Symposium on Visual Computing, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-030-03801-4_41"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"30793","DOI":"10.1007\/s11042-018-6591-3","article-title":"Self-attention recurrent network for saliency detection","volume":"78","author":"Sun","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Miao, Y., Gowayyed, M., and Metze, F. (2015, January 13\u201317). EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding. Proceedings of the 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Scottsdale, AZ, USA.","DOI":"10.1109\/ASRU.2015.7404790"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"39069","DOI":"10.1109\/ACCESS.2019.2902225","article-title":"Recognizing food places in egocentric photo-streams using multi-scale atrous convolutional networks and self-attention mechanism","volume":"7","author":"Sarker","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Salazar, J., Kirchhoff, K., and Huang, Z. (2019). Self-attention networks for connectionist temporal classification in speech recognition. arXiv.","DOI":"10.1109\/ICASSP.2019.8682539"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cross, J., and Huang, L. (2016). Incremental parsing with minimal features using bi-directional LSTM. arXiv.","DOI":"10.18653\/v1\/P16-2006"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, S., Yan, Z., Wu, X., Li, A., and Zhou, B. (2017, January 26\u201329). A method of emotional analysis of movie based on convolution neural network and bi-directional LSTM RNN. Proceedings of the 2017 IEEE Second International Conference on Data Science in Cyberspace (DSC), Shenzhen, China.","DOI":"10.1109\/DSC.2017.15"},{"key":"ref_27","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Verga, P., Strubell, E., and McCallum, A. (2018). Simultaneously self-attending to all mentions for full-abstract biological relation extraction. arXiv.","DOI":"10.18653\/v1\/N18-1080"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, X., Qiu, X., Zhu, C., Liu, P., and Huang, X. (2015, January 17\u201321). Long short-term memory neural networks for Chinese word segmentation. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1141"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TIT.1967.1054010","article-title":"Error bounds for convolutional codes and an asymptotically optimum decoding algorithm","volume":"13","author":"Viterbi","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_31","unstructured":"Levow, G.-A. (2006, January 22\u201323). The third international Chinese language processing bakeoff: Word segmentation and named entity recognition. Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, Sydney, Australia."},{"key":"ref_32","unstructured":"Weischedel, R., Pradhan, S., Ramshaw, L., Palmer, M., Xue, N., Marcus, M., Taylor, A., Greenberg, C., Hovy, E., and Belvin, R. (2011). Ontonotes Release 4.0, Linguistic Data Consortium. LDC2011T03."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Yang, J. (2018). Chinese NER Using Lattice LSTM. arXiv.","DOI":"10.18653\/v1\/P18-1144"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016). Neural architectures for named entity recognition. arXiv.","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, X., Mayhew, S., Sammons, M., and Roth, D. (2018). On the strength of character language models for multilingual named entity recognition. arXiv.","DOI":"10.18653\/v1\/D18-1345"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Greenberg, N., Bansal, T., Verga, P., and McCallum, A. (November, January 31). Marginal likelihood training of BiLSTM-CRF for biomedical named entity recognition from disjoint label sets. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1306"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wu, F., Liu, J., Wu, C., Huang, Y., and Xie, X. (2019). Neural Chinese named entity recognition via CNN-LSTM-CRF and joint training with word segmentation. arXiv.","DOI":"10.1145\/3308558.3313743"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.eswa.2018.07.032","article-title":"Joint entity recognition and relation extraction as a multi-head selection problem","volume":"114","author":"Bekoulis","year":"2018","journal-title":"Expert Syst. Appl."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/1\/30\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:42:20Z","timestamp":1760362940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/1\/30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,5]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["info11010030"],"URL":"https:\/\/doi.org\/10.3390\/info11010030","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,5]]}}}