1 a survey on text classification december 10, 2003 20033077 dongho kim kaist

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A Survey on Text Classification

December 10, 200320033077 Dongho KimKAIST

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Contents

Introduction Statistical Properties of Text Feature Selection Feature Space Reduction Classification Methods Using SVM and TSVM Hierarchical Text Classification Summary

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Introduction

Text classification Assign text to predefined categories based on content Types of text

Documents (typical) Paragraphs Sentences WWW-Sites

Different types of categories By topic By function By author By style

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Text Classification Example

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Computer-Based Text Classification Technologies Naive word-matching (Chute, Yang, & Buntrock 1994)

Finding shared words between the text and names of categories

Weakest method Cannot capture any conceptually relation

Thesaurus-based matching (Lindberg & Humphreys 1990) Using lexical links Insensitive to the context High cost and low adaptivity across domains

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Computer-Based Text Classification Technologies Empirical learning of term-category associations

Learning from a training set Fundamentally different from word-matching

Statistically capturing the semantic association between terms and categories

Context sensitive mapping from terms to categories For example,

Decision tree methods Bayesian belief networks Neural networks Nearest neighbor classification methods Least-squares regression techiniques

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Statistical Properties of Text

There are stable, language-independent patterns in how people use natural language A few words occur very frequently;

most occur rarely In general

Top 2 words : 10~15% of all word occurrences

Top 6 words : 20% of all word occurrences

Top 50 words : 50% of all word occurrences

The 3332

And 2972

A 1775

To 1725

Of 1440

…

Tom 679

Most common words from Tom Sawyer

1

14

8


The most frequent words in one corpus may be rare words in another corpus Example : ‘computer’ in CACM vs. National

Geographic Each corpus has a different, fairly small

“working vocabulary”

These properties hold in a wide range of languages

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Summary : Term usage is highly skewed, but in a

predictable pattern Why is it important to know the

characteristics of text? Optimization of data structures Statistical retrieval algorithms depend on them

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Statistical Profiles

Can act as a summarization device Indicate what a document is about Indicate what a collection is about

1987 WSJ (132MB) 1991 Patent (254MB)

1989 AP (267MB)

stobb (1)stochast (1)stock (46704)stockad (5)stockard (3)stockbridg (2)stockbrok (351)stockbrokag (1)stockbrokerag (101)

sto (1)stochast (21)stochiometr (1)stociometr (1)stock (1910)stockbarg (30)stocker (211)stockholm (1)stockigt (4)

sto (7)sto1 (4)sto3 (1)stoaker (1)stoand (1)stober (6)stocholm (1)stock (28505)stock’ (6)

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Zipf’s Law relates a term’s frequency to its rank Frequency 1/rank There is a constant such that Rank the terms in a vocabulary by frequency, in descending order

Empirical observation : Hence :

for English

Zipf’s Law

krankfrequency k

V

rrrr

r

pNfp

N

rf

1

1 and /

soccurrence wordofnumber total:

rank at termoffrequency :

1.0 ,/ ArApr

ANrfr

A

N

fp r

rr

10/Nk

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Precision and Recall

Recall Percentage of all relevant

documents that are found by a search

Precision Percentage of retrieved

documents that are relevant

collectionin itemsrelevant of #

retrived itemsrelevant of #R

retrived items of #

retrived itemsrelevant of #P

retrieved

+-++-++-

%5.628/5

%5010/5

P

R

Evaluation Metrics

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F-measure

Rewards results that keep recall and precision close together R=40, P=60. R/P average=50. F-measure=48 R=45, P=55. R/P average=50. F-measure=49.5

)(

)(2

RECALLPRESICION

RECALLPRECISIONF

Harmonic average of precision and

recall

Evaluation Metrics

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Break Even Point

The point at which recall equals precision

Evaluation Metrics

Evaluation metric : The value of this

point

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Term Weights: A Brief Introduction The words of a text are not equally indicative of its

meaning

Important: butterflies, monarchs, scientists, direction, compass

Unimportant : most, think, kind, sky, determine, cues, learn

Term weights reflect the (estimated) importance of each term

“Most scientists think that butterflies use the position of the sun in the sky as a kind of compass that allows them to determine which way is north. Scientists think that butterflies may use other cues, such as the

earth’s magnetic field, but we have a lot to learn about monarchs’ sense of direction.”

Feature Selection

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Term Weights

Term frequency (TF) The more often a word occurs in a document,

the better that term is in describing what the document is about

Often normalized, e.g. by the length of the document

Sometimes biased to range [0.4..1.0] to represent the fact that even a single occurrence of a term is a significant event

lengthdocavglengthdoc

tf

tfTF

tf

tfTF

lengthdoc

tfTF

d

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5.15.0

max

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Feature Selection

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Term Weights

Inverse document frequency (IDF) Terms that occur in many documents in the collection are less us

eful for discriminating among documents Document frequency (df) : number of documents containing the t

erm IDF often calculated as

TF and IDF are used in combination as product

1log

df

NIDF

IDFTFxi

Feature Selection

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Vector Space Similarity

Similarity is inversely related to the angle between the vectors

Cosine of the angle between the two vectors

22ii

ii

yx

yx

Feature Selection

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Feature Space Reduction

Main reasons Improve accuracy of the algorithm Decrease the size of data set Control the computation time Avoid overfitting

Feature space reduction technique Stopword removal, stemming Information gain Natural language processing

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Stopword Removal

Stopwords : words that are discarded from a document representation Function words : a, an, and, as, for, in, of, the, to, … About 400 words in English Other frequent words : ‘Lotus’ in a Lotus Support


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Stemming

Group morphological variants Plural : ‘streets’ ‘street’ Adverbs : ‘fully’ ‘full’ Other inflected word forms : ‘goes’ ‘go’ Grouping process is called “conflation”

Current stemming algorithms make mistakes Conflating terms manually is difficult, time-consuming Automatic conflation using rules

Porter Stemmer Porter stemming example : ‘police’, ‘policy’ ‘polic’


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Information Gain

Measuring information obtained by presence or absence of a term in a document

Feature space reduction by thresholding Biased to common term large reduction in size of data

set cannot be achieved

term:

)1(category th :

t

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tcptcptptcptcptpcpcp

tcptptcptpcptIG

1 1 1

))|(log()|()())|(log()|()())(log()(

))|( of(entropy )())|( of(entropy )())( ofentropy ()(


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Natural Language Processing

Pick out the important words from a document For example, nouns, proper nouns, or verbs Ignoring all other parts Not biased to common terms reduction in

bath feature space and size of data Named entities

The subset of proper nouns consisting of people, locations, and organization

Effective in cases of news story classification


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Experimental Results

Data set From six news media sources

Two print sources (New York Times and Associated Press Wire)

Two television sources (ABC World News Tonight and CNN Headline News)

Two radio sources (Public Radio International and Voice of America)

Robert Cooley, Classification of News Stories Using Support Vector Machines,Proceedings of the 16th International Joint Conference on Artificial Intelligence Text Mining Workshop, 1999

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Experimental Results

Results

NLP significant loss in recall and precision SVM >> kNN (using full text or information gain) Binary weighting significant loss in recall

Robert Cooley, Classification of News Stories Using Support Vector Machines,Proceedings of the 16th International Joint Conference on Artificial Intelligence Text Mining Workshop, 1999

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kNN

Stands for k-nearest neighbor classification Algorithms

Given a test document,1. Find k nearest neighbors among training documents2. Calculate and sort score of candidate categories3. Thresholding on these scores

Decision rule

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kNNdi bcdydxsimsigncxy

i

),(),(),(

thresholdspecific-category :

document training theand document test ebetween th similarity the: ),(

}1,0{),(

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b

dxdxsim

cdy

Classification Methods

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LLSF

Stands for Linear Least Squares Fit Obtain matrix of word-category regression coeffici

ents by LLSF FLS : arbitrary document vector of weighted cat

egories By thresholding like kNN, assign categories

2minarg BFAFF

LS

matrixsolution :

)ut vectorsinput/outp ofpair a is columns ding(correspon data training: ,

LSF

BA


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Naïve Bayes

Assumption Words are drawn randomly from class

dependent lexicons (with replacement) Word independence

Result

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ii

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x

YwWPYxXP

iw

xl

1

)|()|(

document in the th word- the:

document in the wordsofnumber total:

1- classpredict else,-

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if 1 classpredict -

11

xx l

ii

l

ii YwWPYPYwWPYP


Classification rule

Word independence

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Estimating the Parameters

Count frequencies in training data

Estimating P(Y) Fraction of positive / negative examples in training data

Estimating P(W|Y) Smoothing with Laplace estimate

Naïve Bayes

yl

ywywTF

negpos

n

y classin documentsin occuring wordsofnumber : -

classin occurs word timesofnumber : ),( -

examples trainingegativepositive/n ofnumber : / -

examples trainingofnumber : -

n

negYP

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posYP )1(ˆ )1(ˆ

2

1),()|(ˆ

yl

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Experiment ResultsYiming Yang and Xin Liu, A re-examination of text categorization methods,Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, 1999.

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Text Classification using SVM

A statistical learning model of text classification with SVMs:

T. Joachims, A Statistical Learning Model of Text Classification with Support Vector Machines,Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2001.

0 if linearly separable

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Properties 1+2: Sparse Examples in High Dimension High dimensional feature vectors (30,000 features) Sparse document vectors : only a few words of the whole language

occur in each document SVMs use overfitting protection which does not depend on the dim

ension of feature


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Property 3: Heterogeneous Use of Words

No pair of documents shares any words, but ‘it’, ‘the’, ‘and’, ‘of’, ‘for’, ‘an’, ‘a’, ‘not’, ‘that’, ‘in’.


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Property 4: High Level of Redundancy

Few features are irrelevant!: Feature space reduction causes loss of information


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Property 5: ‘Zipf’s Law’

Most words occur very infrequently!


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TCat ConceptsT. Joachims, A Statistical Learning Model of Text Classification with Support Vector Machines,Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2001.

occurences contains example negative -

occurences contains example positive -

features includesset th -

])::[],...,::([ 111

i

i

i

sss

n

p

fi

fnpfnpTCat

TCat([20:20:100], # high freq. [4:1:200],[1:4:200],[5:5:600]. # medium freq. [9:1:3000],[1:9:3000],[10:10:4000] # low freq. )

Modeling real text-classification tasksUsed for previous proof

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Margin of Tcat-Concepts

By Zipf’s law, we can bound R2

Intuitively, many words with low frequency relatively short document vectors


cba

bac

2

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ii

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2

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margin : with

0 0 0

,|),(| Inequality Schwarz-Cauchyby 2

ibac

yxyx

Linearly separable

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sphere enclosing of radius the: R

otherwise 1'

)/(1 if '

1

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))((

2

22

1

12

2

CRC

RCCRC

n

ECR

E

hErrE

n

ii

SVMn

Bound on Expected Error of SVM

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Text Classification using TSVM

How would you classify the test set?

Training set {D1, D6} Test set {D2, D3, D4, D5}

T. Joachims, Transductive Inference for Text Classification using Support Vector Machines,Proceedings of the International Conference on Machine Learning (ICML), 1999.

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Why Does Adding Test Examples Reduce Error?


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Experiment Results

Data set Reuter-21578 dataset-ModApte

Training : 9,603 test : 3,299 WebKB collection of WWW pages

Only the class ‘course’, ‘faculty’, ‘project’, ‘student’ are used Stemming and stopword removal are not used

Ohsumed corpus compiled by William Hersh Training : 10,000 test : 10,000


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Experiment Results

Results


P/R-breakeven point for Reuters categories

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Experiment Results

Results


Average P/R-breakeven point on WebKB Average P/R-breakeven point on Ohsumed

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documents

documents

Hierarchical Text Classification

Real world classification complex hierarchical structure

Due to difficulties of training for many classes or features

Class 1Class 1

Class 2Class 2

Class 3Class 3

Level 1

…

Level 2

Class 1-1Class 1-1

Class 1-2Class 1-2

Class 1-3Class 1-3

Class 2-1Class 2-1

…

…

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Hierarchical Text Classification

More accurate specialized classifiers

documents

documents

ComputersComputers

SportsSports

Hardware

Hardware

SoftwareSoftware

ChatChat

SoccerSoccer

FootballFootball‘computer’ :

discriminating

‘computer’ : not discriminating

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Experiment Setting

Data set : LookSmart’s web directory Using short summary from

search engine 370597 unique pages 17173 categories 7-level hierarchy Focus on 13 top-level and 1

50 second-level categories

S. Dumais and H. Chen, Hierarchical classification of Web content. Proceedings of SIGIR'00, August 2000, pp. 256-263.

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Experiment Setting

Using SVM Posterior probabilities by regularized

maximum likelihood fitting Combining probabilities from the first and

second level Boolean scoring function, P(L1) && P(L2) or, Multiplicative scoring function, P(L1) * P(L2)


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Experiment Results

Non-hierarchical (baseline) : F1 = 0.476 Hierarchical

Top-level Training set : F1 = 0.649 Test set : F1 = 0.572

Second-level Multiplicative : F1 = 0.495 Boolean : F1 = 0.497

Assuming top-level classification is correct, F1 = 0.711


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Summary

Feature space reduction Performance of SVM and TSVM is better

than others TSVM has merits in text classification Hierarchical classification is helpful Other issues

Sampling strategies Other kinds of feature selection

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Reference T. Joachims, Text Categorization with Support Vector Machines: Learning

with Many Relevant Features. Proceedings of the European Conference on Machine Learning (ECML), Springer, 1998.

T. Joachims, Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the International Conference on Machine Learning (ICML), 1999.

T. Joachims, A Statistical Learning Model of Text Classification with Support Vector Machines. Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2001.

Robert Cooley, Classification of News Stories Using Support Vector Machines (1999). Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence Text Mining Workshop, August 1999.

Yiming Yang and Xin Liu, A re-examination of text categorization methods. Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, (SIGIR), 1999.


1 a survey on text classification december 10, 2003 20033077 dongho kim kaist

Documents

text classification

characteristics of text

domains slide

style slide

common words

shared words

rare words

frequent words