{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T10:33:45Z","timestamp":1770460425465,"version":"3.49.0"},"reference-count":24,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20180467"],"award-info":[{"award-number":["BK20180467"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>With respect to the fuzzy boundaries of military heterogeneous entities, this paper improves the entity annotation mechanism for entity with fuzzy boundaries based on related research works. This paper applies a BERT\u2010BiLSTM\u2010CRF model fusing deep learning and machine learning to recognize military entities, and thus, we can construct a smart military knowledge base with these entities. Furthermore, we can explore many military AI applications with the knowledge base and military Internet of Things (MIoT). To verify the performance of the model, we design multiple types of experiments. Experimental results show that the recognition performance of the model keeps improving with the increasing size of the corpus in the multidata source scenario, with the <jats:italic>F<\/jats:italic>\u2010score increasing from 73.56% to 84.53%. Experimental results of cross\u2010corpus cross\u2010validation show that the more types of entities covered in the training corpus and the richer the representation type, the stronger the generalization ability of the trained model, in which the recall rate of the model trained with the novel random type corpus reaches 74.33% and the <jats:italic>F<\/jats:italic>\u2010score reaches 76.98%. The results of the multimodel comparison experiments show that the BERT\u2010BiLSTM\u2010CRF model applied in this paper performs well for the recognition of military entities. The longitudinal comparison experimental results show that the <jats:italic>F<\/jats:italic>\u2010score of the BERT\u2010BiLSTM\u2010CRF model is 18.72%, 11.24%, 9.24%, and 5.07% higher than the four models CRF, LSTM\u2010CRF, BiLSTM\u2010CR, and BERT\u2010CRF, respectively. The cross\u2010sectional comparison experimental results show that the <jats:italic>F<\/jats:italic>\u2010score of the BERT\u2010BiLSTM\u2010CRF model improved by 6.63%, 7.95%, 3.72%, and 1.81% compared to the Lattice\u2010LSTM\u2010CRF, CNN\u2010BiLSTM\u2010CRF, BERT\u2010BiGRU\u2010CRF, and BERT\u2010IDCNN\u2010CRF models, respectively.<\/jats:p>","DOI":"10.1155\/2022\/1103022","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T13:05:07Z","timestamp":1642424707000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fusion Deep Learning and Machine Learning for Heterogeneous Military Entity Recognition"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6048-1083","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1935-6237","authenticated-orcid":false,"given":"Lin","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7977-1059","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4324-1627","authenticated-orcid":false,"given":"Ming","family":"Lyu","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"e_1_2_10_1_2","first-page":"11","article-title":"A study on building knowledge base for intelligent battlefield awareness service","volume":"25","author":"Jo S.-H.","year":"2020","journal-title":"Journal of the Korea Society of Computer and Information"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.05.012"},{"key":"e_1_2_10_3_2","doi-asserted-by":"crossref","unstructured":"LiJ.andWangP. 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