[1]卫华,韩立新,夏建华. 基于Word2 fea模型的文本建模方法[J].计算机技术与发展,2016,26(02):165-167.
　WEI Hua,HAN Li-xin,XIA Jian-hua. Text Modeling Method Based on Word2 fea Model[J].,2016,26(02):165-167.
点击复制

 基于Word2 fea模型的文本建模方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年02期
页码:
165-167
栏目:
应用开发研究
出版日期:
2016-02-10

文章信息/Info

Title:
 Text Modeling Method Based on Word2 fea Model
文章编号:
1673-629X（2016)02-0165-03
作者:
 卫华; 韩立新; 夏建华
 河海大学 计算机与信息学院
Author(s):
 WEI Hua; HAN Li-xin; XIA Jian-hua
关键词:
 ; word2vec; 文本建模; 文本分类; word2fea
Keywords:
 ; word2vec; text modeling; text classification; word2fea
分类号:
TP301
文献标志码:
A
摘要:
 文本聚类在数据挖掘和机器学习中发挥着重要作用，该技术经过多年的发展，已产生了一系列的理论成果。传统向量空间模型的文本建模方法存在维度高、数据稀疏和缺乏语义信息等问题，然而仅仅引入词典的文本建模部分解决了语义问题却又受限于人工词典词量少、人工耗力大等多种问题。文中借鉴主题模型的思想，提出一种以word2vec算法得到词向量为基础，词聚类的类别为主题，结合文本中主题的频率、分布范围、位置因子等特征以获得文本在类别空间上的特征向量，完成文本建模的方法word2fea。将其与两种文本建模方法VSM和word2vec base进行比较，实验结果表明该方法能够明显提高文本分类准确率。
Abstract:
 Text classification plays an important role in data mining and machine learning,which has produced a series of theory after years of development. The traditional text modeling method of vector space model has the problems of high dimension,sparse data,and the lack of semantic. However,the text modeling introduced the artificial dictionary is constrained by quantity of words,artificial power consumption and other problems. By referencing the idea of topic model,a text modeling method word2fea was presented which based on the model of word2vec for the topic clusters with the word vectors,meanwhile combined with the frequency,distribution and location of the topic on documents to obtain the feature of the text. Compared with two text modeling methods,VSM and word2vec base,the experi-mental results show that this method can significantly improve the accuracy of text classification.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(02):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(02):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(02):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(02):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(02):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(02):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(02):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(02):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(02):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(02):47.
[11]张兴兰,刘炀. 基于复杂网络及神经网络挖掘用户兴趣的方法[J].计算机技术与发展,2016,26(12):22.
　ZHANG Xing-lan,LIU Yang. Method of Mining User Interest Based on Complex Network and Neural Network[J].,2016,26(02):22.

更新日期/Last Update: 2016-04-15