English and Taiwanese text categorization using N-gram based on Vector Space Model

2010 ◽  
Author(s):  
Makoto Suzuki ◽  
Naohide Yamagishi ◽  
Yi-Ching Tsai ◽  
Takashi Ishida ◽  
Masayuki Goto
Keyword(s):  
Vector Space ◽  
Text Categorization ◽  
Vector Space Model ◽  
Space Model ◽  
N Gram

scholarly journals A Comprehensive Comparative Study Using Vector Space Model with K-Nearest Neighbor on Text Categorization Data

2007 ◽  
Vol 2 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Wa`el Musa Hadi ◽  
Fadi Thabtah ◽  
Salahideen Mousa ◽  
Samer Al Hawari ◽  
Ghassan Kanaan ◽  
...  
Keyword(s):  
Comparative Study ◽  
Vector Space ◽  
Text Categorization ◽  
Nearest Neighbor ◽  
Vector Space Model ◽  
K Nearest Neighbor ◽  
Space Model

A Text Categorization Method Based on SVM and Improved K-Means

2013 ◽  
Vol 427-429 ◽  
pp. 2449-2453
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Hu Li
Keyword(s):  
Support Vector Machine ◽  
Vector Space ◽  
High Performance ◽  
Supervised Classification ◽  
Text Categorization ◽  
Clustering Algorithm ◽  
Vector Space Model ◽  
Classification Method ◽  
Support Vector ◽  
Space Model

Traditional supervised classification method such as support vector machine (SVM) could achieve high performance in text categorization. However, we should first hand-labeled the samples before classifying. Its a time-consuming task. Unsupervised method such as k-means could also be used for handling the text categorization problem. However, Traditional k-means could easily be affected by several isolated observations. In this paper, we proposed a new text categorization method. First we improved the traditional k-means clustering algorithm. The improved k-means is used for clustering vectors in our vector space model. After that, we use the SVM to categorize vectors which are preprocessed by improved k-means. The experiments show that our algorithm could out-perform the traditional SVM text categorization method.

A CONCEPT VECTOR SPACE MODEL FOR SEMANTIC KERNELS

2009 ◽  
Vol 18 (02) ◽  
pp. 239-272 ◽  
Author(s):  
SUJEEVAN ASEERVATHAM
Keyword(s):  
Vector Space ◽  
Language Processing ◽  
Text Categorization ◽  
Semantic Analysis ◽  
Similarity Measures ◽  
Vector Space Model ◽  
Inner Product ◽  
Support Vector ◽  
Linear Kernel ◽  
Space Model

Kernels are widely used in Natural Language Processing as similarity measures within inner-product based learning methods like the Support Vector Machine. The Vector Space Model (VSM) is extensively used for the spatial representation of the documents. However, it is purely a statistical representation. In this paper, we present a Concept Vector Space Model (CVSM) representation which uses linguistic prior knowledge to capture the meanings of the documents. We also propose a linear kernel and a latent kernel for this space. The linear kernel takes advantage of the linguistic concepts whereas the latent kernel combines statistical and linguistic concepts. Indeed, the latter kernel uses latent concepts extracted by the Latent Semantic Analysis (LSA) in the CVSM. The kernels were evaluated on a text categorization task in the biomedical domain. The Ohsumed corpus, well known for being difficult to categorize, was used. The results have shown that the CVSM improves performance compared to the VSM.

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