基于描述文本和实体标签的网络视频分类算法
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摘要
目前,各大社交平台和视频点播网站的网络视频数量出现了爆炸式的增长,如何快速准确地对这些网络视频进行归类和管理成为了研究的热点问题﹒为了较好地解决这种分类任务,文中提出了基于描述文本和实体标签的网络视频分类算法,该算法结合了描述文本内容和知识图谱中的实体标签来构造文档-特征矩阵﹒实验结果表明使用了实体标签的视频分类算法性能更好,平均精确率和平均召回率以及平均F1值比未使用实体标签的视频分类算法要高2%以上﹒
At present there has been an explosive growth in the number of web video on major social platforms and video on demand web sites. How to quickly and accurately classify and manage these web videos has become a hot spot of research. In order to solve this classification task, a web video classification algorithm based on description text and entity tag was proposed in this paper. The algorithm combines the description text and the entity tags in the knowledge graph to construct a document-feature matrix. The experimental results show that the video classification algorithm using the entity tag shows better performance, and the average precision and average recall and the average F1 value are higher 2% than the video classification algorithm of the unused entity tag.
At present there has been an explosive growth in the number of web video on major social platforms and video on demand web sites. How to quickly and accurately classify and manage these web videos has become a hot spot of research. In order to solve this classification task, a web video classification algorithm based on description text and entity tag was proposed in this paper. The algorithm combines the description text and the entity tags in the knowledge graph to construct a document-feature matrix. The experimental results show that the video classification algorithm using the entity tag shows better performance, and the average precision and average recall and the average F1 value are higher 2% than the video classification algorithm of the unused entity tag.
引文
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[7]刘璐,贾彩燕.基于文本扩展模型的网络视频聚类方法[J].智能系统学报,2017,12(6):799-805.
[8]朱义明.基于社交信息的网络视频分类[D].成都:西南交通大学,2011.
[9]桑苗杰.基于语义的视频内容标注与检索系统研究与实现[D].北京:北京工业大学,2015.
[10]何春辉,李云翔,王孟然,等.改进的Text Rank双层单文档摘要提取算法[J].湖南城市学院学报:自然科学版,2017,26(6):55-60.
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[2]艾丽丽,孙明远,孙健.基于文本描述的视频分类系统建模[J].信息通信,2013(04):63-64.
[3]陈芬,赖茂生.多特征视频分类挖掘实验研究[J].现代图书情报技术,2012(05):76-80.
[4]艾丽丽.基于文本挖掘的视频资源分类研究[D].成都:电子科技大学,2013.
[5]吴雨希.基于文本挖掘的视频标签生成及视频分类研究[D].上海:上海交通大学,2015.
[6]齐全,董晶.基于描述能力的视频标题分类[J].华南理工大学学报:自然科学版,2011,39(7):134-139.
[7]刘璐,贾彩燕.基于文本扩展模型的网络视频聚类方法[J].智能系统学报,2017,12(6):799-805.
[8]朱义明.基于社交信息的网络视频分类[D].成都:西南交通大学,2011.
[9]桑苗杰.基于语义的视频内容标注与检索系统研究与实现[D].北京:北京工业大学,2015.
[10]何春辉,李云翔,王孟然,等.改进的Text Rank双层单文档摘要提取算法[J].湖南城市学院学报:自然科学版,2017,26(6):55-60.
[11]郭显娥,武伟,刘春贵,等.多类SVM分类算法的研究[J].山西大同大学学报:自然科学版,2010,26(3):6-8.
[12]SUN J Y.Jieba中文分词组件[EB/OL].(2017-08-28)[2018-04-27].https://github.com/fxsjy/jieba.
[13]PEDREGOSA F,VAROQUAUX G,GRAMFORT A,et al.Scikit-learn:machine learning in Python[J].Journal of Machine Learning Research,2011,12:2825-2830.