Inductive Inference for Large Scale Text Classification

Kernel Approaches and Techniques

By: Catarina Silva, Bernadete Ribeiro

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Hardcover | 13 November 2009

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176 Pages

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23.5 x 15.88 x 1.27

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Text classification is becoming a crucial task to analysts in different areas. In the last few decades, the production of textual documents in digital form has increased exponentially. Their applications range from web pages to scientific documents, including emails, news and books. Despite the widespread use of digital texts, handling them is inherently difficult - the large amount of data necessary to represent them and the subjectivity of classification complicate matters.

This book gives a concise view on how to use kernel approaches for inductive inference in large scale text classification; it presents a series of new techniques to enhance, scale and distribute text classification tasks. It is not intended to be a comprehensive survey of the state-of-the-art of the whole field of text classification. Its purpose is less ambitious and more practical: to explain and illustrate some of the important methods used in this field, in particular kernel approaches and techniques.

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Fundamentals
Background on Text Classification	p. 3
Problem Setting	p. 3
Applications of Text Classification	p. 5
Document Organization	p. 5
Text Filtering	p. 5
Word Sense Disambiguation	p. 5
Other Applications	p. 6
Document Representation	p. 6
Pre-processing Text	p. 8
Feature Selection	p. 8
Feature Extraction	p. 10
Classifiers	p. 11
Rocchio's Method	p. 12
Decision Trees and Rules	p. 13
Naïve Bayes	p. 15
K-Nearest Neighbor	p. 15
Neural Networks	p. 16
Kernel-Based Learning Machines	p. 17
Committees	p. 18
Active Learning	p. 20
Other Methods	p. 21
Evaluation	p. 21
Performance Criteria	p. 22
Document Corpora	p. 24
Evaluation of Pre-processing Methods	p. 26
Conclusion	p. 29
Kernel Machine for Text Classification	p. 31
Kernel Methods	p. 31
Support Vector Machines	p. 32
Linear Hard-Margin SVMs	p. 33
Soft-Margin SVMs	p. 36
Nonlinear SVMs	p. 37
Relevance Vector Machines	p. 38
Bayesian Approaches	p. 39
RVM Approach	p. 40
Baseline Kernel Machines Performances with Benchmark Corpora	p. 43
SVM Performance	p. 44
RVM Performance	p. 46
Discussion	p. 47
Conclusion	p. 48
Approaches and Techniques
Enhancing SVMs for Text Classification	p. 51
Incorporating Unlabeled Data	p. 51
Background Knowledge and Active Learning	p. 53
Experimental Results	p. 56
Combining Background Knowledge and Active Learning	p. 59
Analysis of Results	p. 60
Using Multiple Classifiers	p. 63
SVM Ensembles	p. 65
Experimental Results and Analysis	p. 66
Conclusion	p. 69
Scaling RVMs for Text Classification	p. 71
Introduction	p. 71
Scale Reduction Approaches	p. 72
Active Learning	p. 73
Similitude Measure	p. 76
Divide-and-Conquer Approaches	p. 78
Incremental RVM	p. 79
RVM Boosting	p. 80
RVM Ensemble	p. 83
Analysis of Results	p. 84
Hybrid RVM-SVM Approach	p. 86
Conclusion	p. 89
Distributing Text Classification in Grid Environments	p. 93
Introduction	p. 93
Related Work	p. 94
Distributed Computing Platforms	p. 94
Distributed Applications	p. 95
Deployment in the Distributed Environment	p. 97
Task Scheduling and Direct Acyclic Graphs	p. 97
DAG Design in a Distributed Environment	p. 97
Distributed Environment for the Experimental Setup	p. 100
Model of the Environment	p. 100
Design of Distributed Text Classification Scheduling Schemes	p. 102
Dataflow in Text Classification	p. 102
Optimization of Scheduling Schemes	p. 104
Experimental Results	p. 108
Processing Time	p. 109
Classification Performance	p. 112
Discussion of Results	p. 114
Conclusion	p. 115
Framework for Text Classification	p. 117
Novel Trends in Text Classification	p. 122
Information Semantics	p. 123
Information Extraction	p. 124
Information Distributed Systems	p. 126
Conclusion	p. 127
REUTERS-21578	p. 129
Introduction	p. 120
History	p. 129
Formatting	p. 130
The REUTERS Tag	p. 130
Document-Internal Tags	p. 132
Categories	p. 133
Using Reuters-21578 for Text Categorization Research	p. 134
The Modified Lewis ("ModLewis") Split	p. 135
The Modified Apte ("ModApte") Split	p. 135
Stopwords	p. 137
RCV1 - Reuters Corpus Volume I	p. 139
Introduction	p. 139
The Documents	p. 139
The Categories	p. 140
Topic Codes	p. 140
Coding Policy	p. 142
Stopwords	p. 142
References	p. 143
Index	p. 153
Table of Contents provided by Ingram. All Rights Reserved.

Inductive Inference for Large Scale Text Classification

Kernel Approaches and Techniques

At a Glance

Hardcover

Shipping

How to return your order

Defective items

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