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Sholom M. Weiss
•
Nitin
Indurkhya
Fundamentals of Predictive Text
Mining
& Springer
•
Tong Zhang
Contents
1
Overview of Text 1.1
1.2
2
Mining
1
What's
Special About Text Mining?
1.1.1
Structured
1.1.2
Is Text Different from Numbers?
or
Unstructured Data?
1
2 3
What Types of Problems Can Be Solved?
5
1.3
Document Classification
6
1.4
Information Retrieval
1.5
Clustering and Organizing
1.6
Information Extraction
1.7
Prediction and Evaluation
1.8
The Next
1.9
Summary
6 Documents
7 8
Chapters
9 10 10
1.10 Historical and Bibliographical Remarks
11
1.11
12
Questions
and Exercises
From Textual Information to Numerical Vectors
13
2.1
Collecting
13
2.2
Document Standardization
15
2.3
Tokenization
16
2.4
Lemmatization
2.5
Documents
17
2.4.1
Inflectional
2.4.2
Stemming
Stemming
to a
Root
2.7 2.8 2.9 2.10
19
Vector Generation for Prediction
21
2.5.1
26
Multiword Features
2.5.2 2.6
19
Labels for the Right Answers 2.5.3 Feature Selection by Attribute Ranking Sentence Boundary Determination
Part-of-Speech Tagging Word Sense Disambiguation Phrase Recognition Named Entity Recognition
28
29 29 31 32 32 33 ix
Contents
x
2.11
33
Parsing
2.12 Feature Generation
35 36
2.13 Summary 2.14 Historical and 2.15 3
Questions
Bibliographical
41
3.3
Document Classification
43
3.4
Learning 3.4.1 Similarity
42 44
and
Nearest-Neighbor Similarity
Methods
45
3.4.2
Document
3.4.3
Decision Rules
48
3.4.4
Decision Trees
54
3.4.5
Scoring by
3.4.6
Linear
46
Probabilities
55
Scoring Methods
58
Evaluation of Performance
Estimating
3.5.2
66
Current and Future Performance
3.7
Getting the Applications Summary
3.8
Historical and Bibliographical Remarks
3.9
Questions
3.6
39
Predict from Text
to
3.5.1
Most from
a
Learning
Method
and Exercises
66
69 69
70 70 72
Information Retrieval and Text Mining 4.1 Is Information Retrieval a Form of Text Mining?
75
4.2
Key Word Search Nearest-Neighbor Methods Measuring Similarity
76
4.4.1
Shared Word Count
78
4.4.2
Word Count and Bonus
78
4.4.3
Cosine
79
4.3 4.4
4.5
Similarity
75
77 78
Web-based Document Search
80
4.5.1
81
Link
Analysis
4.6
Document
4.7
Inverted Lists
85
4.8
Evaluation of Performance
87
4.9
Summary
88
Matching
4.10 Historical and 4.11 5
38
and Exercises
Using Text for Prediction 3.1 Recognizing that Documents Fit a Pattern How Many Documents Are Enough? 3.2
3.5
4
36
Remarks
Bibliographical Remarks
85
88
and Exercises
89
Finding Structure in a Document Collection 5.1 Clustering Documents by Similarity 5.2 Similarity of Composite Documents 5.2.1 jt-Means Clustering
91
Questions
93 94 96
Contents
Hierarchical
5.2.3
The EM
What Do
a
Clustering
99
Algorithm
102
Cluster's Labels Mean?
105
5.4
Applications
107
5.5
Evaluation of Performance
108
5.6
Summary
110
5.7
Historical and Bibliographical Remarks Questions and Exercises
Ill
Looking for Information
110
in Documents
113
6.1
Goals of Information Extraction
113
6.2
Finding Patterns and Entities from Text 6.2.1 Entity Extraction as Sequential Tagging 6.2.2 Tag Prediction as Classification 6.2.3 The Maximum Entropy Method 6.2.4 Linguistic Features and Encoding
115
6.3
6.2.5
Local
6.2.6
Global
Sequence
116 117 118 123
Prediction Models
124
Sequence Prediction Models
128
Coreference and Relationship Extraction 6.3.1 Coreference Resolution
129
6.3.2
131
6.4
Relationship Extraction Template Filling and Database Construction
6.5
Applications
133
129 132
6.5.1
Information Retrieval
133
6.5.2
134
6.5.3
Commercial Extraction Systems Criminal Justice
6.5.4
Intelligence
6.6
Summary
6.7
Historical and
6.8 7
5.2.2
5.3
5.8
6
xi
Questions
135 135 136
Bibliographical
Remarks
137
and Exercises
138
Data Sources for Prediction: Databases, Hybrid Data and the Web 7.1 Ideal Models of Data
.
7.1.1
Ideal Data for Prediction
141
7.1.2
Ideal Data for Text and Unstructured Data
142
7.1.3
7.2 7.3
141 141
Hybrid and Mixed Data Practical Data Sourcing
142 144
Prototypical Examples 7.3.1 Web-based Spreadsheet Data
146
7.3.2
Web-based XML Data
146
7.3.3
Opinion
Data and Sentiment
145
Analysis
7.4
Hybrid Example: Independent
7.5
Mixed Data in Standard Table Format
7.6
Summary
7.7
Historical and
7.8
Questions and Exercises
Sources of Numerical and Text Data
148
151 152 153
Bibliographical Remarks
154 154
Contents
xii
8
157
Case Studies 8.1
8.2
Market Intelligence from the Web
157
8.1.1
The Problem
157
8.1.2
Solution Overview
158
8.1.3
Methods and Procedures
'59
8.1.4
System Deployment
'60
Lightweight Document Matching for Digital Libraries
