Large Scale Text Categorization Based on Density Statistics Merging

2019 ◽  
pp. 321-327
Author(s):  
Rujuan Wang ◽  
Suhua Wang
Keyword(s):  
Text Categorization ◽  
Large Scale

scholarly journals Large-Scale Bayesian Logistic Regression for Text Categorization

Technometrics ◽  
2007 ◽  
Vol 49 (3) ◽  
pp. 291-304 ◽  
Author(s):  
Alexander Genkin ◽  
David D Lewis ◽  
David Madigan
Keyword(s):  
Logistic Regression ◽  
Text Categorization ◽  
Large Scale ◽  
Bayesian Logistic Regression

scholarly journals Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Lin Guo ◽  
Wanli Zuo ◽  
Tao Peng ◽  
Lin Yue
Keyword(s):  
Text Categorization ◽  
Large Scale ◽  
Semantic Information ◽  
Semantic Knowledge ◽  
Semistructured Data ◽  
Experimental Results ◽  
Semantic Relations ◽  
Lexical Analysis ◽  
Tree Shape ◽  
Implicit Information

The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness.

Web Text Categorization Based on Statistical Merging Algorithm in Big Data Environment

2019 ◽  
Vol 10 (3) ◽  
pp. 17-32 ◽  
Author(s):  
Rujuan Wang ◽  
Gang Wang
Keyword(s):  
Complex Network ◽  
Text Classification ◽  
Text Categorization ◽  
Large Scale ◽  
Feature Selection Method ◽  
Point Of View ◽  
Classification Method ◽  
Data Sampling ◽  
Modern Information Technology ◽  
Text Feature

In the field of modern information technology, how to find information quickly, accurately and comprehensively that users really needed has become the focus of research in this field. In this article, a feature selection method based on a complex network is proposed for the structure and content characteristics of large-scale web text information. The preprocessed web text is converted into a complex network. The nodes in the network correspond to the entries in the text. The edges of the network correspond to the links between the entries in the text, and the degree of nodes and the aggregation system are used. Second, the text classification method is studied from the point of view of data sampling, and a text classification method based on density statistics is proposed. This method uses not only the density information of the text feature set in the classification process, but also the use of statistical merging criteria to get the text. The difference information of each feature has a better classification effect for large text collections.

Exploiting semantic resources for large scale text categorization

2012 ◽  
Vol 39 (3) ◽  
pp. 763-788 ◽  
Author(s):  
Jian Qiang Li ◽  
Yu Zhao ◽  
Bo Liu
Keyword(s):  
Text Categorization ◽  
Large Scale ◽  
Semantic Resources

HYBRID DECISION TREE ARCHITECTURE UTILIZING LOCAL SVMs FOR EFFICIENT MULTI-LABEL LEARNING

2013 ◽  
Vol 27 (07) ◽  
pp. 1351004 ◽  
Author(s):  
DEJAN GJORGJEVIKJ ◽  
GJORGJI MADJAROV ◽  
SAŠO DŽEROSKI
Keyword(s):  
Decision Tree ◽  
Text Categorization ◽  
Large Scale ◽  
Semantic Annotation ◽  
Predictive Performance ◽  
Tree Architecture ◽  
Support Vector ◽  
Svm Classifier ◽  
Strong Impact ◽  
Classification Problems

Multi-label learning (MLL) problems abound in many areas, including text categorization, protein function classification, and semantic annotation of multimedia. Issues that severely limit the applicability of many current machine learning approaches to MLL are the large-scale problem, which have a strong impact on the computational complexity of learning. These problems are especially pronounced for approaches that transform MLL problems into a set of binary classification problems for which Support Vector Machines (SVMs) are used. On the other hand, the most efficient approaches to MLL, based on decision trees, have clearly lower predictive performance. We propose a hybrid decision tree architecture, where the leaves do not give multi-label predictions directly, but rather utilize local SVM-based classifiers giving multi-label predictions. A binary relevance architecture is employed in the leaves, where a binary SVM classifier is built for each of the labels relevant to that particular leaf. We use a broad range of multi-label datasets with a variety of evaluation measures to evaluate the proposed method against related and state-of-the-art methods, both in terms of predictive performance and time complexity. Our hybrid architecture on almost every large classification problem outperforms the competing approaches in terms of the predictive performance, while its computational efficiency is significantly improved as a result of the integrated decision tree.

Semi-supervised learning in large scale text categorization

2017 ◽  
Vol 22 (3) ◽  
pp. 291-302 ◽  
Author(s):  
Zewen Xu ◽  
Jianqiang Li ◽  
Bo Liu ◽  
Jing Bi ◽  
Rong Li ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Text Categorization ◽  
Large Scale

scholarly journals A sparse version of the ridge logistic regression for large-scale text categorization

2011 ◽  
Vol 32 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Sujeevan Aseervatham ◽  
Anestis Antoniadis ◽  
Eric Gaussier ◽  
Michel Burlet ◽  
Yves Denneulin
Keyword(s):  
Logistic Regression ◽  
Text Categorization ◽  
Large Scale
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