{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T15:52:04Z","timestamp":1770738724303,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,15]],"date-time":"2020-01-15T00:00:00Z","timestamp":1579046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science Fund Project","award":["61303097"],"award-info":[{"award-number":["61303097"]}]},{"name":"the Shanghai Science Fund Project","award":["17ZR1428400"],"award-info":[{"award-number":["17ZR1428400"]}]},{"name":"the Second (2016) Shanghai Research Project for Private University","award":["2016-SHNGE-08ZD"],"award-info":[{"award-number":["2016-SHNGE-08ZD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Usually taken as linguistic features by Part-Of-Speech (POS) tagging, Named Entity Recognition (NER) is a major task in Natural Language Processing (NLP). In this paper, we put forward a new comprehensive-embedding, considering three aspects, namely character-embedding, word-embedding, and pos-embedding stitched in the order we give, and thus get their dependencies, based on which we propose a new Character\u2013Word\u2013Position Combined BiLSTM-Attention (CWPC_BiAtt) for the Chinese NER task. Comprehensive-embedding via the Bidirectional Llong Short-Term Memory (BiLSTM) layer can get the connection between the historical and future information, and then employ the attention mechanism to capture the connection between the content of the sentence at the current position and that at any location. Finally, we utilize Conditional Random Field (CRF) to decode the entire tagging sequence. Experiments show that CWPC_BiAtt model we proposed is well qualified for the NER task on Microsoft Research Asia (MSRA) dataset and Weibo NER corpus. A high precision and recall were obtained, which verified the stability of the model. Position-embedding in comprehensive-embedding can compensate for attention-mechanism to provide position information for the disordered sequence, which shows that comprehensive-embedding has completeness. Looking at the entire model, our proposed CWPC_BiAtt has three distinct characteristics: completeness, simplicity, and stability. Our proposed CWPC_BiAtt model achieved the highest F-score, achieving the state-of-the-art performance in the MSRA dataset and Weibo NER corpus.<\/jats:p>","DOI":"10.3390\/info11010045","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T04:14:41Z","timestamp":1579234481000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["CWPC_BiAtt: Character\u2013Word\u2013Position Combined BiLSTM-Attention for Chinese Named Entity Recognition"],"prefix":"10.3390","volume":"11","author":[{"given":"Shardrom","family":"Johnson","sequence":"first","affiliation":[{"name":"School of Optical-electrical and Computer Engineering, University of Shanghai for Science and Technology, Jungong Road 516, Shanghai 200093, China"},{"name":"XianDa College of Economics and Humanities, Shanghai International Studies University, East Tiyuhui Road 390, Shanghai 200083, China"},{"name":"Information Centre, Shanghai Municipal Education Commission, Dagu Road 100, Shanghai 200003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sherlock","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Optical-electrical and Computer Engineering, University of Shanghai for Science and Technology, Jungong Road 516, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanchen","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Foreign Languages, Ningbo University, Fenghua Road 818, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Peng, N., and Dredze, M. (2015). Named entity recognition for Chinese social media with jointly trained embeddings. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics.","DOI":"10.18653\/v1\/D15-1064"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xu, L., Liu, K., Zeng, D., and Zhao, J. (2015). Event extraction via dynamic multi-pooling 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 (Volume 1: Long Papers), Association for Computational Linguistics.","DOI":"10.3115\/v1\/P15-1017"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bunescu, R.C., and Mooney, R.J. (2005). A shortest path dependency kernel for relation extraction. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, Association for Computational Linguistics.","DOI":"10.3115\/1220575.1220666"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Miwa, M., and Bansal, M. (2016). 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), Association for Computational Linguistics.","DOI":"10.18653\/v1\/P16-1105"},{"key":"ref_5","first-page":"1375","article-title":"Local and global algorithms for disambiguation to Wikipedia","volume":"Volume 1","author":"Ratinov","year":"2011","journal-title":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gupta, N., Singh, S., and Roth, D. (2017). Entity linking via joint encoding of types, descriptions, and context. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics.","DOI":"10.18653\/v1\/D17-1284"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yao, X., and Van Durme, B. (2014). Information extraction over structured data: Question answering with freebase. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics.","DOI":"10.3115\/v1\/P14-1090"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Isozaki, H., and Kazawa, H. (2002). Efficient support vector classifiers for named entity recognition. Proceedings of the 19th International Conference on Computational Linguistics\u2014Volume 1, Association for Computational Linguistics.","DOI":"10.3115\/1072228.1072282"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kazama, J., Makino, T., Ohta, Y., and Tsujii, J. (2002). Tuning support vector machines for biomedical named entity recognition. Proceedings of the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain\u2014Volume 3, Association for Computational Linguistics.","DOI":"10.3115\/1118149.1118150"},{"key":"ref_10","first-page":"589","article-title":"Named entity recognition using support vector machine: A language independent approach","volume":"4","author":"Ekbal","year":"2010","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, G., and Su, J. (2002). Named entity recognition using an HMM-based chunk tagger. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics.","DOI":"10.3115\/1073083.1073163"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Florian, R., Ittycheriah, A., Jing, H., and Zhang, T. (2003). Named entity recognition through classifier combination. Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003\u2014Volume 4, Association for Computational Linguistics.","DOI":"10.3115\/1119176.1119201"},{"key":"ref_13","unstructured":"Lafferty, J.D., McCallum, A., and Pereira, F.C.N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of the Eighteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc."},{"key":"ref_14","first-page":"119","article-title":"Conditional random field based named entity recognition in geological text","volume":"1","author":"Sobhana","year":"2010","journal-title":"Int. J. Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","article-title":"Neural networks and physical systems with emergent collective computational abilities","volume":"79","author":"Hopfield","year":"1982","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_16","unstructured":"Donahoe, J.W., and Dorsel, V.P. (1997). Serial order: A parallel distributed processing approach. Neural-Network Models of Cognition, North-Holland Publishing. Advances in Psychology Series Volume 121."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_19","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_20","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cao, P., Chen, Y., Liu, K., Zhao, J., and Liu, S. (2018). Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics.","DOI":"10.18653\/v1\/D18-1017"},{"key":"ref_22","unstructured":"Chen, A., Peng, F., Shan, R., and Sun, G. (2006). Chinese named entity recognition with conditional probabilistic models. Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, Association for Computational Linguistics."},{"key":"ref_23","unstructured":"Zhou, J., He, L., Dai, X., and Chen, J. (2006). Chinese named entity recognition with a multi-phase model. Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, Association for Computational Linguistics."},{"key":"ref_24","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\u2013decoder for statistical machine translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, H., and Sun, X. (2017). F-score driven max margin neural network for named entity recognition in Chinese social media. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Association for Computational Linguistics.","DOI":"10.18653\/v1\/E17-2113"},{"key":"ref_26","unstructured":"Zhu, Y., Wang, G., and Karlsson, B.F. (2019). CAN-NER: Convolutional attention network for Chinese named entity recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Peng, N., and Dredze, M. (2016). Improving named entity recognition for Chinese social media with word segmentation representation learning. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguistics.","DOI":"10.18653\/v1\/P16-2025"},{"key":"ref_28","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C. (2014). Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018). Deep contextualized word representations. arXiv.","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref_31","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_32","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (2014). Recurrent Models of Visual Attention. Advances in Neural Information Processing Systems 27, Curran Associates, Inc."},{"key":"ref_33","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., and Xu, B. (2016). Attention-based bidirectional long short-term memory networks for relation classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics.","DOI":"10.18653\/v1\/P16-2034"},{"key":"ref_35","unstructured":"McCallum, A., Freitag, D., and Pereira, F.C.N. (2000). Maximum entropy Markov models for information extraction and segmentation. Proceedings of the Seventeenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","unstructured":"Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., and Dyer, C. (2016). 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, Association for Computational Linguistics.","DOI":"10.18653\/v1\/N16-1030"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, H., and Sun, X. (2017, January 4\u20139). A unified model for cross-domain and semi-supervised named entity recognition in Chinese social media. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10977"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, S., Zhao, Z., Hu, R., Li, W., Liu, T., and Du, X. (2018). Analogical reasoning on Chinese morphological and semantic relations. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Association for Computational Linguistics.","DOI":"10.18653\/v1\/P18-2023"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Han, A.L.-F., Zeng, X., Wong, D.F., and Chao, L.S. (2015). Chinese named entity recognition with graph-based semi-supervised learning model. Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing, Association for Computational Linguistics.","DOI":"10.18653\/v1\/W15-3103"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Yang, J. (2018). Chinese NER using lattice LSTM. arXiv.","DOI":"10.18653\/v1\/P18-1144"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/1\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:05:13Z","timestamp":1760364313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/11\/1\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,15]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["info11010045"],"URL":"https:\/\/doi.org\/10.3390\/info11010045","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,15]]}}}