@inproceedings{a7a735f550d1442fa969a3f94a7ac357,
title = "Entity Recognition for Military Situation Awareness Knowledge Graph with Wikipedia Data",
abstract = "Entity recognition is an essential component of knowledge representation and knowledge extraction research. To enhance military situation awareness through the construction of a knowledge graph, this paper presents a novel method, BERTATT-POSBiLSTMLSTMCRF, which is based on the traditional entity recognition model BERT-BiLSTM-CRF. The local location information and the impact of the entity's position in the sentence on the entity recognition task are both fully considered by introducing the attention mechanism. Additionally, an LSTM layer is added after the BiLSTM layer to deal with long-distance label dependencies while improving the model's ability to recognize long entities. Comparative experiments demonstrate that the improved model proposed in this paper is effective in entity recognition with Wikipedia data.",
keywords = "entity recognition, knowledge graph, military situation awareness",
author = "Linxiu Chen and Weili Guan and Xudong Guo and Yuan Li",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",
year = "2023",
doi = "10.1109/CCDC58219.2023.10327056",
language = "English",
series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4352--4357",
booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",
address = "United States",
}