Short text expansion and classification based on pseudo-relevance feedback" /> Short text expansion and classification based on pseudo-relevance feedback" /> Short text expansion and classification based on pseudo-relevance feedback" /> 基于伪相关反馈的短文本扩展与分类
Please wait a minute...
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Short text expansion and classification based on pseudo-relevance feedback
WANG Meng, LIN Lan-fen, WANG Feng
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Download:   PDF(1851KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A novel classification method based on pseudo-relevance feedback (PFR) was proposed in order to solve the sparseness problems in short text classification. The short texts were expanded using the web pages which are similar to them in semantic level. The feature vector generation algorithm was modified to extract both the local features and the global features. The method can alleviate the sparseness problem of the final feature matrix, which is common in short text classification because of the limited length of the texts. The experimental results on an open dataset show that the method can significantly improve the short text classification effect compared with state-of-the-art methods.



Published: 26 November 2014
CLC:  TP 391  
Cite this article:

WANG Meng, LIN Lan-fen, WANG Feng.

Short text expansion and classification based on pseudo-relevance feedback
. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2014, 48(5): 2-.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2014.10.000     OR     http://www.zjujournals.com/eng/Y2014/V48/I5/2


基于伪相关反馈的短文本扩展与分类

针对短文本分类问题,提出基于伪相关反馈(PFR)的短文本扩展与分类方法.在保持语义不变的情况下,利用互联网中的相似语料对短文本的内容进行了扩展.对现有的仅使用局部特征的扩展语料特征抽取方法进行改进,引入全局特征抽取,将全局特征与局部特征相结合得到了更好的特征向量,有效地解决了分类过程中由短文本长度有限导致的特征矩阵高度稀疏的问题.通过在开放数据集上的测试和与其他文献的结果比对,验证了该方法在短文本分类的问题上可以取得较好的效果.
[1] 张传强,洪慧,金红光. 聚光式太阳能热发电技术发展状况[J]. 热力发电, 2010, 39(12): 5-9.
ZHANG Chuan-qiang, HONG Hui, JIN Hong-guang. Development situation of power generation technology using heat of light-concentrating solar energy [J].Thermal Power Generation, 2010, 39(12): 5-9.
[2] 章国芳,朱天宇,王希晨. 塔式太阳能热发电技术进展及在我国的应用前景[J]. 太阳能, 2008, 29 (11): 33-37.
ZHANG Guo-fang, ZHU Tian-yu, WANG Xi-chen. Development of solar power tower technology and its prospect in China [J]. Solar Energy, 2008, 29 (11): 33-37.
[3] 高维,徐蕙,徐二树,等.塔式太阳能热发电吸热器运行安全性研究[J].中国电机工程学报, 2013, 33(2): 92-97.
GAO Wei, XU Hui, XU Er-shu, et al. Research on operation security of solar thermal tower power plant receiver [J]. Proceedings of the CSEE, 2013, 33(2): 92-97.
[4] GONZALEZ M M, PALAFOX H J, CLAUDIO E A. Numerical study of heat transfer by natural convection and surface thermal radiation in an open cavity receiver [J]. Solar Energy, 2012, 86(4): 1118-1128.
[5] GARBRECHT O, SIBAI A F, KNEER R, et al. CFD-simulation of a new receiver design for a molten salt solar power tower [J]. Solar Energy, 2013, 90(1): 94-106.
[6] XU E, YU Q, WANG Z, et al. Modeling and simulation of 1 MW DAHAN solar thermal power tower plant [J]. Renewable Energy, 2011, 36(2): 848-857.
[7] 方嘉宾,魏进家,董训伟,等. 腔式太阳能吸热器热性能的模拟计算[J]. 工程热物理学报, 2009, 30(3): 428432.
FANG Jia-bin,WEI Jin-jia,DONG Xun-wei,et al. Performance simulation of solar cavity receiver [J]. Journal of Engineering Thermophysics, 2009, 30(3): 428-432.
[8] LU Jian-feng, DING Jing, YANG Jian-ping. Heat transfer performance of an external receiver pipe under unilateral concentrated solar radiation [J]. Solar Energy, 2010, 84(11): 1879-1887.
[9] 杨敏林,杨晓西,丁静,等. 半周加热半周绝热的熔盐吸热管传热特性研究[J]. 太阳能学报, 2009, 30(8): 10071012.
YANG Min-lin,YANG Xiao-xi,DING Jing,et al. Heat transfer research on molten salt receiver with semi-circumference heat [J]. Acta Energiae Solaris Sinica, 2009, 30(8): 1007-1012.
[10] 杜景龙,唐大伟,李铁. 5kW聚光型太阳模拟器加热特性的实验研究[J]. 太阳能学报, 2012, 33(4): 625-629.
DU Jing-long,TANG Da-wei,LI Tie. Experiment study of the heating characteristics of 5kW focused solar simulator [J]. Acta Energiae Solaris Sinica, 2012, 33(4): 625-629.
[11] 刘志刚,张春平,赵耀华,等. 一种新型腔式吸热器的设计与实验研究[J]. 太阳能学报, 2005, 26(3): 3843.
LIU Zhi-gang, ZHANG Chun-ping, ZHAO Yao-hua, et al. The design and experiments of a new cavity absorber [J]. Acta Energiae Solaris Sinica, 2005, 26(3): 38-43.
[12] PRAKASH M, KEDARE S B, NAYAK J K. Investigations on heat losses from a solar cavity receiver [J]. Solar Energy, 2009, 83(2): 157-170.
[13] WU S, XIAO L, CAO Y, et al. Convection heat loss from cavity receiver in parabolic dish solar thermal power system: a review [J]. Solar Energy, 2010, 84(8): 1342-1355.
[14] SIEBERS D L, KRAABEL J S. Estimating convective energy losses from solar central receivers [R]. Livermore: Sandia National Labs, 1984.
[15] LI X, KONG W, WANG Z, et al. Thermal model and thermodynamic performance of molten salt cavity receiver [J]. Renewable Energy, 2010, 35(5): 981-988.
[16] XIAO G, GUO K, LUO Z, et al. Simulation and experimental study on a spiral solid particle solar receiver [J]. Applied Energy, 2014, 113(01): 178188.
[17] REDDY K S, KUMAR S N. Combined laminar natural convection and surface radiation heat transfer in a modified cavity receiver of solar parabolic dish [J]. International Journal of Thermal Sciences, 2008, 47(12): 1647-1657.
[1] HE Xue-jun, WANG Jin, LU Guo-dong, LIU Zhen-yu, CHEN Li, JIN Jing. 3D head portrait sculpture by industrial robot based on triangular mesh slicing and collision detection[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1104-1110.
[2] WANG Hua, HAN Tong-yang, ZHOU Ke. KeyGraph-based community detection algorithm for public security intelligence[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1173-1180.
[3] YOU Hai-hui, MA Zeng-yi, TANG Yi-jun, WANG Yue-lan, ZHENG Lin, YU Zhong, JI Cheng-jun. Soft measurement of heating value of burning municipal solid waste for circulating fluidized bed[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(6): 1163-1172.
[4] BI Xiao-jun, WANG Jia-hui. Teaching-learning-based optimization algorithm with hybrid learning strategy[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(5): 1024-1031.
[5] WANG Liang, YU Zhi-wen, GUO Bin. Moving trajectory prediction model based on double layer multi-granularity knowledge discovery[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 669-674.
[6] LIAO Miao, ZHAO Yu-qian, ZENG Ye-zhan, HUANG Zhong-chao, ZHANG Bing-kui, ZOU Bei-ji. Automatic segmentation for cell images based on support vector machine and ellipse fitting[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 722-728.
[7] MU Jing-jing, ZHAO Xin-yue, HE Zai-xing, ZHANG Shu-you. Contour reconstruction of overlapped bubbles based on concave-convex transformation and circle fitting[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 714-721.
[8] HUANG Zheng-yu, JIANG Xin-long, LIU Jun-fa, CHEN Yi-qiang, GU Yang. Fusion feature based semi-supervised manifold localization method[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 655-662.
[9] JIANG Xin-long, CHEN Yi-qiang, LIU Jun-fa, HU Li-sha, SHEN Jian-fei. Wearable system to support proximity awareness for people with autism[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 637-647.
[10] DAI Cai-yan, CHEN Ling, LI Bin, CHEN Bo-lun. Sampling-based link prediction in complex networks[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 554-561.
[11] LIU Lei, YANG Peng, LIU Zuo-jun. Locomotion-Mode recognition using multiple kernel relevance vector machine[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 562-571.
[12] GUO Meng-li, DA Fei-peng, DENG Xing, GAI Shao-yan. 3D face recognition based on keypoints and local feature[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 584-589.
[13] WANG Hai jun, GE Hong juan, ZHANG Sheng yan. Fast object tracking algorithm via kernel collaborative presentation[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(2): 399-407.
[14] ZHANG Ya nan, CHEN De yun, WANG Ying jie, LIU Yu peng. Incremental graph pattern matching based dynamic recommendation method for cold-start user[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(2): 408-415.
[15] LIU Yu peng, QIAO Xiu ming, ZHAO Shi lei, MA Chun guang. Deep combination of large-scale features in statistical machine translation[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(1): 46-56.