{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:09:47Z","timestamp":1766297387370,"version":"3.41.0"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62076015"],"award-info":[{"award-number":["62076015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2022,11,30]]},"abstract":"<jats:p>In recent years, a large number of Chinese electronic texts have been produced in the process of information construction in various fields. Identifying specific entities in these electronic texts has become a major research focus. Most existing research methods use radicals to extract the glyph features of Chinese characters but have seen its limitation. This paper extracts the features of Chinese characters from three aspects: glyph features, phonetic features, and character features, and improves conventional feature extraction methods for each kind of feature. A new named entity recognition method (AIP) is proposed by transforming Chinese characters into corresponding images for glyph feature extraction, dividing pinyin into initials, vowels, and tones for phonetic feature extraction, and fine-tuning the A Lite Bert model for character feature extraction to improve the performance of the model. This paper compares the performance of the AIP model and mainstream neural network models on Chinese named entity recognition tasks on commonly used data sets and the data sets in specific domains. The results showed that AIP achieved better results than the related work. The F1 values on the two data sets are 94.4% and 80.5%, respectively, which validates the model's versatility.<\/jats:p>","DOI":"10.1145\/3522736","type":"journal-article","created":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T21:26:58Z","timestamp":1647379618000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["AIP: A Named Entity Recognition Method Combining Glyphs and Sounds"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2393-117X","authenticated-orcid":false,"given":"Bo","family":"Liu","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing, China and School of Mathematical and Computational Sciences, Massey University, Palmerston North, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2571-9436","authenticated-orcid":false,"given":"Zhuo","family":"Su","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4047-9514","authenticated-orcid":false,"given":"Guangzhi","family":"Qu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Oakland University, Rochester, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50496-4_20"},{"issue":"2","key":"e_1_3_1_3_2","first-page":"339","article-title":"Named entity recognition approaches","volume":"8","author":"Mansouri A.","year":"2008","unstructured":"A. Mansouri, L. S. Affendey, and A. Mamat. 2008. Named entity recognition approaches. International Journal of Computer Science and Network Security 8, 2 (2008), 339\u2013344.","journal-title":"International Journal of Computer Science and Network Security"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/1089815.1089819"},{"key":"e_1_3_1_5_2","first-page":"118","volume-title":"Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing","author":"Chen W.","year":"2006","unstructured":"W. Chen, Y. Zhang, and H. Isahara. 2006. Chinese named entity recognition with conditional random fields. In Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing. 118\u2013121."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/WISA.2017.8"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_1_8_2","unstructured":"Z. Huang W. Xu and K. Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv :1508.01991."},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"X. Ma and E. Hovy. 2016. End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv :1603.01354.","DOI":"10.18653\/v1\/P16-1101"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Y. Zhang and J. Yang. 2018. Chinese NER using Lattice LSTM. arXiv preprint arXiv :1805.02023.","DOI":"10.18653\/v1\/P18-1144"},{"key":"e_1_3_1_11_2","first-page":"4982","volume-title":"IJCAI","author":"Gui T.","year":"2019","unstructured":"T. Gui, R. Ma, Q. Zhang, L. Zhao, Y. G. Jiang, and X. Huang. 2019. CNN-based Chinese NER with lexicon rethinking. In IJCAI. 4982\u20134988."},{"key":"e_1_3_1_12_2","unstructured":"Y. Zhu G. Wang and B. F. Karlsson. 2019. CAN-NER: Convolutional attention network for Chinese named entity recognition. arXiv preprint arXiv :1904.02141."},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-14932-0_78"},{"key":"e_1_3_1_14_2","first-page":"2532","volume-title":"LREC","author":"Li H.","year":"2014","unstructured":"H. Li, M. Hagiwara, Q. Li, and H. Ji. 2014. Comparison of the impact of word segmentation on name tagging for Chinese and Japanese. In LREC. 2532\u20132536."},{"key":"e_1_3_1_15_2","first-page":"855","volume-title":"Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)","author":"Lu Y.","year":"2016","unstructured":"Y. Lu, Y. Zhang, and D. Ji. 2016. Multi-prototype Chinese character embedding. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16). 855\u2013859."},{"key":"e_1_3_1_16_2","unstructured":"J. Devlin M. W. Chang K. Lee and K. Toutanova. 2018. BeERTPre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv :1810.04805."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/ITME.2019.00022"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-12640-1_34"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Y. Li W. Li F. Sun and S. Li. 2015. Component-enhanced Chinese character embeddings. arXiv preprint arXiv :1508.06669.","DOI":"10.18653\/v1\/D15-1098"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3358528.3358562"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-2098"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2891838"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_1_24_2","unstructured":"Z. Lan M. Chen S. Goodman K. Gimpel P. Sharma and R. Soricut. 2019. Albert: A Lite BERT for self-supervised learning of language representations. arXiv preprint arXiv :1909.11942."},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32381-3_16"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3038670"},{"key":"e_1_3_1_28_2","unstructured":"Y. Meng W. Wu F. Wang X. Li P. Nie F. Yin and J. Li. 2019. Glyce: Glyph-vectors for Chinese character representations. arXiv preprint arXiv:1901.10125 ."},{"key":"e_1_3_1_29_2","first-page":"28","volume-title":"China Conference on Knowledge Graph and Semantic Computing","author":"Xuan Z.","year":"2020","unstructured":"Z. Xuan, R. Bao, and S. Jiang. 2020. FGN: Fusion glyph network for Chinese named entity recognition. In China Conference on Knowledge Graph and Semantic Computing. Springer, Singapore, 28\u201340."},{"key":"e_1_3_1_30_2","doi-asserted-by":"crossref","unstructured":"Z. Sun X. Li X. Sun Y. Meng X. Ao Q. He and J. Li. 2021. ChineseBERT: Chinese pretraining enhanced by glyph and pinyin information. arXiv preprint arXiv:2106.16038 .","DOI":"10.18653\/v1\/2021.acl-long.161"},{"key":"e_1_3_1_31_2","unstructured":"J. Lafferty A. McCallum and F. C. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data."}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3522736","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3522736","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:16Z","timestamp":1750188616000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3522736"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,12]]},"references-count":30,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,11,30]]}},"alternative-id":["10.1145\/3522736"],"URL":"https:\/\/doi.org\/10.1145\/3522736","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2022,11,12]]},"assertion":[{"value":"2021-08-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-25","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-11-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}