专利成果
近年来代表性专利:
(1) 黄少滨; 朴秀峰; 申林山; 毛瑞雪; 刘国峰; 刘建华; 一种基于结构化查询语言语句的源信息追踪方法, 2012-6-13, 中国, CN201110434707.X. (专利)
(2) 黄少滨; 何荣博; 申林山; 李熔盛; 一种四险一金领域知识图谱中实体对齐方法,2020-9, 中国, 202010990634.1. (专利)
(3) 黄少滨; 余日昌; 刘汪洋; 杨辉; 李熔盛; 申林山; 李轶; 张柏嘉; 一种面向特定领域开放网络问句的文本分类方法, 2019-12-03, 中国, 2019112228685. (专利)
(4) 黄少滨; 李轶; 李熔盛; 申林山; 何杰; 一种领域术语语义漂移抽取方法, 2019-12-03, 中国, 2019112228793. (专利)
(5) 黄少滨; 张幻; 程序; 严江; 申林山; 李熔盛; 一种汉语自然语言文本的词语切分方法, 2019-12-03, 中国, 2019112235458. (专利)
(6) 黄少滨; 吴汉瑜; 李熔盛; 申林山; 姜梦奇; 范贺添; 谷虹润; 一种基于神经网络的文本分类方法, 2019-12-03, 中国, 201911223541.X. (专利)
(7) 黄少滨; 四险一金领域知识图谱web应用软件V1.0, 2020SR1621666, 原始取得,全部权利, 2020-08. (软件著作权)
发表论文
近年来代表性论文:
(1) Huang, S., Huang, H., Chen, Z., Lv, T., & Zhang, T. (2012). Lazy slicing for state-space exploration. Journal of Computer Science and Technology, 27(4), 872-890.
(2) 黄少滨; 杨欣欣; 吕天阳; 郑纬民; 基于理想点的星型高阶联合聚类一致融合策略, 计算机学报, 2015, (07): 1460-1472.
(3) 黄少滨; 程媛; 万庆生; 刘国峰; 申林山; 一种基于IDEF1x模型的层次多关系聚类算法, 自动化学报, 2014, (08): 1740-1753.
(4) Rongsheng Li; Qinyong Yu; Shaobin Huang; Linshan Shen; Chi Wei;Jingyun Sun; Phrase embedding learning from internal and external information based on autoencoder, Information Processing and Management, 2021, 58.
(5) Rongsheng Li; Shaobin Huang; Xiangke Mao; Jie He; Linshan Shen; TransPhrase: A new method for generating phrase embedding from word embedding in Chinese, Expert Systems with Applications, 2021, 168.
(6) Huang, H., Huang, S., Chen, Z., & Zhang, T. (2011, October). Model reduction using the orthogonality between overapproximate slicing and abstract. In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI) (Vol. 4, pp. 2077-2081). IEEE.
(7) 黄少滨, 刘国峰, 万庆生, 程媛, & 申林山. (2013). 一种基于部分已验证匹配关系的模式匹配模型. 自动化学报, 39(10), 1642-1652.
(8) 杨欣欣, & 黄少滨. (2015). 高阶异构数据层次联合聚类算法. 计算机研究与发展, 52(1),200
(9) Huang, S., Lv, T., Zhang, X., Yang, Y., Zheng, W., & Wen, C. (2014). Identifying node role in social network based on multiple indicators. PloS one, 9(8), e103733.
(10) Mao, X., Yang, H., Huang, S., Liu, Y., & Li, R. (2019). Extractive summarization using supervised and unsupervised learning. Expert Systems with Applications, 133, 173-181.