Text Classification with Support Vector Machine

1
Text Classification with Support Vector Machine

• Xuemei Li
• Department of Computer Science
• Old Dominion University
• April 24, 2006

2
Outline

• Problem and Approach
• Support Vector Machine General
• Architecture
• Preliminary Results
• Normalization and Parameter Selection
• Parameter Selection Experiments
• Conclusion

3
Objective

• Problem
• a collection of xml documents, and
• Each document contains an average of 2700 words
• a list of subject areas
• 25 broad subject fields, divided into
• 251 narrower groups
• Classification Assign each document to the most
  pertinent subject area (s)

4
Input Xml Document Sample
  lt?xml version"1.0" ?gt - lt!-- XML document
generated using OCR technology from ScanSoft,
Inc.   --gt - ltdocument ssdoc-vers"SSDOC1.0"
ocr-vers"OmniPage Pro 14" xmlns"x-schemahttp//
www.scansoft.com/omnipage/xml/ssdoc-schema2.xml"gt
- ltpage width"12240" height"15840" x-res"300"
y-res"300" bpp"1" orientation"0" skew"0"
filename"C\testbed\OCR\firstlast\ADA424473.pdf"
language"0"gt - ltregion reg-type"horizontal"gt  
ltrc l"620" t"14784" r"11572" b"15139" /gt -
ltparagraph para-type"text" align"left"
left-indent"0" right-indent"0" start-indent"0"
line-spacing"180"gt - ltln baseline"15029"
ff"Times New Roman" fs"600" char-attr"bold"gt  
ltwd l"715" t"14923" r"1094" b"15072"
char-attr"non-bold"gtAprillt/wdgt   ltwd l"1142"
t"14923" r"1478" b"15038" char-attr"non-bold"gt
2004lt/wdgt   ltwd l"9720" t"14904" r"10397"
b"15082" fs"800" char-attr"italic"gtDefenselt/wdgt
  ltwd l"10430" t"14904" r"11160" b"15038"
fs"1000"gtHorizonslt/wdgt   ltwd l"11371"
t"14856" r"11477" b"15034" fs"1100"gt1lt/wdgt
5
Sample Subject Fields Groups

• 01--Aviation Technology
• 01 Aerodynamics
• 02 Military Aircraft Operations
• 03 Aircraft
• 01 Helicopters
• 02 Bombers
• 02--Agriculture
• 03--Astronomy and Astrophysics
• 04--Atmospheric Sciences
• 05--Behavioral and Social Sciences
• 06--Biological and Medical Sciences
• 07--Chemistry
• 08--Earth Sciences and Oceanography

6
Approach

• Our approach Support Vector Machine
• training SVM for each subject area
• Existing library
• LibSVM http//www.csie.ntu.edu.tw/cjlin/libsvm/
• different SVM formulations
• cross validation for model selection
• both C and Java sources
• Other project in our research group used LibSVM
  before
• Rainbow http//www.cs.umass.edu/mccallum/bow/

7
Implementation Technology

• JAVA jdk1.4.2_06
• IDE Eclipse3.0.2
• Commons Digester 1.7
• Lucene 1.4.3
• LibSVM

8
Support Vector Machine -General

• Overview
• A learning method introduced by V. Vapnik
• It is widely used in pattern recognition areas
  such as face detection, isolated handwriting
  digit recognition, gene classification, etc.
• Key Ideas
• Maximize Margins
• Construct Kernels
• A list of SVM applications is available at
  http//www.clopinet.com/isabelle/Projects/SVM/appl
  ist.html

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Support Vector Machine Linear Separator

• Many decision boundaries can separate these two
  classes
• Which one should we choose?

http//www.cs.utexas.edu/mooney/cs391L/svm.ppt
10
Support Vector Machine Maximize Margins
Basic idea Choose the one to separate two
classes with largest margin
http//www.cs.utexas.edu/mooney/cs391L/svm.ppt
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Non-linear Separator

• A curve is needed to fully separate the green and
  red objects

http//www.statsoft.com/textbook/stsvm.html
12
The Kernel Trick

• General idea the original feature space can
  always be mapped to some higher-dimensional
  feature space where the training set is separable

http//www.statsoft.com/textbook/stsvm.html
13
Architecture
Terms
Features/weights
Xml files
TF
Document Representation
LibSVM Train
Lucene
DF
label
SVM Model
Features
Features/weights
Xml files
Terms
TF
Document Representation
LibSVM Predictor
Lucene
DF
Prediction
14
Number of Terms in Document

• Original Xml file
• Including duplicated terms
• Before LibSVM
• Including only unique terms

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Document Representation

• Different kinds of documents contain different
  terms and these term occurrences can be viewed as
  clues for document classification
• TF (Term Frequency)
• Each distinct word corresponds to a feature
• The number of times word occurs in the document
  as its value
• TFIDF (Term Frequency Inverse Document Frequency)
• Each distinct word corresponds to a feature
• TF(log(N/DF)1)

16
Lucene

• Lucene is a high-performance text search engine
  and is suitable for cross-platform full-text
  search application.
• It provides direct API for TF and DF.
• built-in standard analyzer of Lucene
• This analyzer is a JavaCC-based parser
• with rules for email addresses, acronyms,
  hostnames, floating point numbers
• stop word removal.

17
Data Format of LibSVM

• Training Data File(.trn)
• label featureIdfeatureValue
  featureIdfeatureValue
• Each document example starts with a label, 1 or
  -1, followed by a list of feature id value
  pairs.
• Feature id number must be greater than 0
• Test Data File(.lst)
• Similar to training data file

18
LibSVM

• 4 SVM types
• C-SVM classification
• The range of C is from zero to infinity
• Nu-SVM classification
• Basically it is the same thing as C-SVM but with
  different parameters
• The range of nu is always between0,1
• Nu is related to the ratio of support vectors and
  the ratio of the training error.
• Epsilon-SVM regression
• Nu-SVM regression

19
LibSVM

• 4 kernels
• Linear a special case of RBF
• Polynomial
• If a high degree is used, numerical difficulties
  may happen
• RBF
• a reasonable first choice
• Sigmoid
• In general its accuracy is not better than RBF

20
Testbed
21
Baseline SVM Evaluation

• 16 subject categories with more than 150 samples
• Determine accuracy
• Assume P is the number of positives in test set,
  N is the number of negatives in test set.
• Assume NP is the number of right classified
  positives, MN is the number of right classified
  negatives, then
• Accuracy (NPMN) / (PN)

22
Preliminary Results
23
Normalization and Parameter Selection
Terms
Features/weights
Xml files
TF
Document Representation
Lucene
Grid.py Tool
Normalization
DF
label
Best C and Gamma
Features
LibSVM Train
Ranges
Xml files
Terms
SVM Model
Features/weights
TF
Document Representation
LibSVM Predictor
Normalization
Lucene
DF
24
A practical Guide to Support Vector
Classification

• Transform data to the format of an SVM software
• Conduct simple scaling/normalization on the data
• Consider the RBF kernel
• Grid.py
• Use crosss-validation to find the best parameter
  C and Gamma
• Use the best parameter C and Gamma to train the
  training set
• Test

25
Experiment Results (category 120500)
26
Experiment Results (category 150100)
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Experiment Results (category 050300)
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Conclusion

• Baseline results
• The average accuracy of half of 16 categories are
  greater than 70
• 6 of 16 categories are between 65-70
• 2 of 16 are between 60-65
• Normalization and Parameter Selection Experiments
• Accuracy can be improved 7-10 by normalization
  with best c and gamma

29
Thanks

• Advisor Dr.Maly
• Dr.Zubair
• Dr.Zeil