Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 9-12.

• Intelligent Computing • Previous Articles     Next Articles

Military Domain Named Entity Recognition Based on Multi-label

SHAN Yi-dong, WANG Heng-jun, WANG Na   

  1. (The Third Institute,Information Engineering University,Zhengzhou 450001,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: In order to identify military named entities in military texts,this paper classified them into six categories according to the characteristics of military named entities.On this basis,in order to further solve the problem that the multi-nested and combined composite military named entities are difficult to identify,the traditional annotation method was improved,and a multi-label annotation method was proposed.First,the compound military named entity is divided into several words,so that it becomes a combination of multiple minimum phrases,and then each part of the phrase is segmented according to its position in the named entity.On the basis of segmentation,each word in each phrase is marked with a vocabulary based on its position in the phrase.Finally,the entire label is ultimately used as the labeling result for each word in the military named entity.The experimental results show that the annotation method can enhance the recognition effect of military named entities.

Key words: Composite military named entity, Military named entity, Multi-label

CLC Number: 

  • TP391
[1]田俊玮.军事领域中文术语抽取的研究[D].大连:大连理工大学,2013.
[2]冯蕴天,张宏军,郝文宁.面向军事文本的命名实体识别[J].计算机科学,2015,42(7):15-18,47.
[3]蒋超.研报领域的产品词命名实体识别的研究[D].南宁:广西大学,2017.
[4]姜文志,顾佼佼,胡文萱,等.基于多模型结合的军事命名实体识别[J].兵工自动化,2011,30(10):90-93.
[5]孙安,于英香,罗永刚,等.序列标注模型中的字粒度特征提取方案研究——以CCKS2017:Task2临床病历命名实体识别任务为例[J].图书情报工作,2018,62(11):103-111.
[6]章成志,苏新宁.基于条件随机场的自动标引模型研究[J].中国图书馆学报,2008(5):89-94,99.
[7]王学锋,杨若鹏,朱巍.基于深度学习的军事命名实体识别方法[J].装甲兵工程学院学报,2018,32(4):94-98.
[8]秦杰,曹雷,彭辉,等.一种面向军事文本的领域特征词向量描述方法[J].计算机工程,2016,42(8):160-165.
[9]谢志宁.中文命名实体识别算法研究[D].杭州:浙江大学,2017.
[10]高强,游宏梁.基于层叠模型的国防领域命名实体识别研究[J].现代图书情报技术,2012(11):47-52.
[11]乌兰敖日格乐.中文军事组织机构名的识别[D].大连:大连理工大学,2010.
[12]张磊.特定领域命名实体识别通用方法的研究[D].北京:北京交通大学,2018.
[13]周练.Word2vec的工作原理及应用探究[J].科技情报开发与经济,2015,25(2):145-148.
[14]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[15]BOUTHILLIER X,KONDA K,VINCENT P,et al.Dropout as data augmentation[J].arXiv:1508.08700.
[16]单赫源,张海粟,吴照林.小粒度策略下基于CRFs的军事命名实体识别方法[J].装甲兵工程学院学报,2017,31(1):84-89.
[1] WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[2] ZHU Xu-dong, XIONG Yun. Study on Multi-label Image Classification Based on Sample Distribution Loss [J]. Computer Science, 2022, 49(6): 210-216.
[3] SUN Lin, HUANG Miao-miao, XU Jiu-cheng. Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief [J]. Computer Science, 2022, 49(4): 152-160.
[4] FANG Zhong-li, WANG Zhe, CHI Zi-qiu. Dual-stream Reconstruction Network for Multi-label and Few-shot Learning [J]. Computer Science, 2022, 49(1): 212-218.
[5] LI Ke-yue, CHEN Yi, NIU Shao-zhang. Social E-commerce Text Classification Algorithm Based on BERT [J]. Computer Science, 2021, 48(2): 87-92.
[6] CHEN Jie-ting, WANG Wei-ying, JIN Qin. Multi-label Video Classification Assisted by Danmaku [J]. Computer Science, 2021, 48(1): 167-174.
[7] WANG Qing-song, JIANG Fu-shan, LI Fei. Multi-label Learning Algorithm Based on Association Rules in Big Data Environment [J]. Computer Science, 2020, 47(5): 90-95.
[8] LIU Xiao-ling,LIU Bai-song,WANG Yang-yang,TANG Hao. Research and Development of Multi-label Generation Based on Deep Learning [J]. Computer Science, 2020, 47(3): 192-199.
[9] ZHU Zhi-cheng, LIU Jia-wei, YAN Shao-hong. Research and Application of Multi-label Learning in Intelligent Recommendation [J]. Computer Science, 2019, 46(11A): 189-193.
[10] GE Hong-kong, LUO Heng-li, DONG Jia-yuan. Face Attributes in Wild Based on Deep Learning [J]. Computer Science, 2019, 46(11A): 246-250.
[11] WEN Wen, CHEN Ying, CAI Rui-chu, HAO Zhi-feng, WANG Li-juan. Emotion Classification for Readers Based on Multi-view Multi-label Learning [J]. Computer Science, 2018, 45(8): 191-197.
[12] CHEN Fu-cai, LI Si-hao, ZHANG Jian-peng, HUANG Rui-yang. Multi-label Feature Selection Algorithm Based on Improved Label Correlation [J]. Computer Science, 2018, 45(6): 228-234.
[13] TANG Yi-ping, WANG Li-ran, HE Xia, CHEN Peng, YUAN Gong-ping. Classification of Tongue Image Based on Multi-task Deep Convolutional Neural Network [J]. Computer Science, 2018, 45(12): 255-261.
[14] SUN Lin, PAN Jun-fang, ZHANG Xiao-yu, WANG Wei and XU Jiu-cheng. Multi-label-specific Feature Selection Method Based on Neighborhood Rough Set [J]. Computer Science, 2018, 45(1): 173-178.
[15] LIN Meng-lei, LIU Jing-hua, WANG Chen-xi and LIN Yao-jin. Multi-label Feature Selection Algorithm Based on Label Weighting [J]. Computer Science, 2017, 44(10): 289-295.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!