Download Machine Learning Methods for Text / Web Data Mining Overview

Machine Learning Methods
for
Text / Web Data Mining
Byoung-Tak Zhang
School of Computer Science and Engineering
Seoul National University
E-mail: [email protected]
This material is available at
http://scai.snu.ac.kr./~btzhang/
Overview
z
Introduction
4Web Information Retrieval
4Machine Learning (ML)
4ML Methods for Text/Web Data Mining
z
Text/Web Data Analysis
4Text Mining Using Helmholtz Machines
4Web Mining Using Bayesian Networks
z
Summary
4Current and Future Work
2
1
Web Information Retrieval
Classification System
Preprocessing and Indexing
Text Data
Text Classification
Information Filtering
Information Extraction
DB Template Filling
& Information
Extraction System
Information Filtering System
DB Record
Location
user profile
filtered data
Date
question
answer
feedback
DB
3
Machine Learning
z
Supervised Learning
4Estimate an unknown mapping from known inputoutput pairs
4Learn fw from training set D={(x,y)} s.t. f w (x) = y = f (x)
4Classification: y is discrete
4Regression: y is continuous
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Unsupervised Learning
4Only input values are provided
4Learn fw from D={(x)} s.t. f w (x) = x
4Density Estimation
4Compression, Clustering
4
2
Machine Learning Methods
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Neural Networks
4 Multilayer Perceptrons (MLPs)
4 Self-Organizing Maps (SOMs)
4 Support Vector Machines (SVMs)
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Probabilistic Models
4 Bayesian Networks (BNs)
4 Helmholtz Machines (HMs)
4 Latent Variable Models (LVMs)
z
Other Machine Learning Methods
4 Evolutionary Algorithms (EAs)
4 Reinforcement Learning (RL)
4 Boosting Algorithms
4 Decision Trees (DTs)
5
ML for Text/Web Data Mining
z
z
z
z
z
z
z
Bayesian Networks for Text Classification
Helmholtz Machines for Text Clustering/Categorization
Latent Variable Models for Topic Word Extraction
Boosted Learning for TREC Filtering Task
Evolutionary Learning for Web Document Retrieval
Reinforcement Learning for Web Filtering Agents
Bayesian Networks for Web Customer Data Mining
6
3
Preprocessing for Text Learning
From: [email protected]
Newsgroups:comp.graphics
Subject: Need specs on Apple
QT
I need to the specs, or at least a
very verbose interpretation of the
specs, for QuckTime. Technical
articles from magazines and
references to books would be
nice, too.
I also need the specs in a format
usable on a Unix or MS-Dos
system. I can’t do much with the
QuickTime stuff they have on..
0 baseball
0 car
0 clinton
0 computer
0 graphics
0 hockey
2 quicktime
.
.
1 references
0 space
3 specs
1 unix
.
7
Text Mining: Data Sets
z
Usenet Newsgroup Data
420 categories
41000 documents for each category
420000 documents in total.
z
TDT2 Corpus
4Target detection and tracking (TDT): NIST
4Used 6,169 documents in experiments
8
4
Text Mining:
Helmholtz Machine Architecture
h1
h2
…
hm
: recognition weight
: generative weight
P (hi = 1) =
d1
d2
d3
dn
…
P (d i = 1) =
1
n


1 + exp − bi − ∑ wij d j 
j =1


1
m


1 + exp − bi − ∑ wij h j 
j
=
1


4Latent nodes
4 [Chang and Zhang, 2000]
4 Input nodes
• Binary values
• Extract the underlying causal
structure in the document set.
• Capture correlations of the words
in documents.
• Binary values
• Represent the existence or
absence of words in documents.
9
Text Mining:
Learning Helmholtz Machines
4Introduce a recognition network for estimation of a
generative network.
( ) ( ( ) )
T

 T

P d (t ) ,α (t ) | θ 
log( D | θ ) = ∑ log ∑ P d ( t ) , α (t ) | θ  = ∑ log ∑ Q α ( t )

Q α (t )
t =1
α ( t )
 t =1
α (t )

(t )
(t )
T
,
|
P
d
α
θ
≥ ∑∑ Q α (t ) log
Q α (t )
t =1 α ( t )
(
( )
)
(
( )
)
4Wake-Sleep Algorithm
• Train the recognition and generative models alternately.
• Update the weight in network iteratively by simple local delta rule.
wijnew = wijold + ∆wij
∆wij = γ si ( s j − p( s j = 1))
10
5
Text Mining: Methods
z Text
Categorization
4 Train a Helmholtz machine for each category.
4 Total N machines for N categories.
4 Once the N machines have been estimated, classification of a test
document proceeds by estimating the likelihood of the document
for each machine.
cˆ = arg max[log P(d | c)]
c∈C
z Topic
Words Extraction
4 For the entire document sets, train a Helmholtz machine.
4 After training, examine the weights of connections from a latent
node to input nodes, that is words.
11
Text Mining: Categorization Results
4Usenet Newsgroup Data
• 20 categories, 1000 documents for each category, 20000 documents in
total.
12
6
Text Mining: Topic Words Extraction Results
4TDT2 Corpus
46,169 documents
1
tabacco, smoking, gingrich, newt, trent, republicans, congressional, republicans, attorney,
smokers, lawsuit, senate, cigarette, morris, nicotine
2
warplane, airline, saudi, gulf, wright, soldiers, yitzhak, tanks, stealth, sabah, stations, kurds,
mordechai, separatist, governor
3
olympics, nagano, olympic, winter, medal, hockey, atheletes, cup, games, slalom, medals, bronze,
skating, lillehammer, downhill
4
netanyahu, palestinian, arafat, israeli, yasser, kofi, annan, benjamin, palestinians, mideast, gaza,
jerusalem, eu, paris, israel
5
India, pakistan, pakistani, delhi, hindu, vajpayee, nuclear, tests, atal, kashmir, indian, janata,
bharatiya, islamabad, bihari
6
Suharto, habibie, demonstrators, riots, indonesians, demonstrations, soeharto, resignation,
jakarta, rioting, electoral, rallies, wiranto, unrest, megawati
7
imf, monetary, currencies, currency, rupiah, singapore, bailout, traders, markets, thailand,
inflation, investors, fund, banks, baht
8
pope, cuba, cuban, embargo, castro, lifting, cubans, havana, alan, invasion, reserve, paul, output,
vatican, freedom
13
Web Mining: Customer Analysis
z KDD-2000
Web Mining Competition
4Data: 465 features over 1700 customers
• Features include friend promotion rate, date visited,
weight of items, price of house, discount rate, …
• Data was collected during Jan. 30 – March 30, 2000
• Friend promotion was started from Feb. 29 with TV
advertisement.
4Aims: Description of heavy/low spenders
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7
15
16
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Web Mining: Feature Selection
z
Features selected by various ways [Yang & Zhang, 2000]
DecisionTree+Factor
Analysis
Decision Tree
Discriminant Model
V368 (Weight Average)
V243 (OrderLine Quantity Sum)
V245 (OrderLine Quantity
Maximum)
F1 = 0.94*V324 + 0.868*V374
+ 0.898*V412
F2 = 0.829*V234 +
0.857*V240
F3 = -0.795*V237+
0.778*V304
V13 (SendEmail)
V234 (OrderItemQuantity Sum%
HavingDiscountRange(5 . 10))
V237 (OrderItemQuantitySum%
Having DiscountRange(10.))
V240 (Friend)
V243 (OrderLineQuantitySum)
V245 (OrderLineQuantity Maximum)
V304 (OrderShippingAmtMin)
V324 (NumLegwearProduct
Views)
V368 (Weight Average)
V374 (NumMainTemplateViews)
V412 (NumReplenishable
Stock Views)
V240 (Friend)
V229 (Order-Average)
V304 (OrderShippingAmtMin.)
V368 (Weight Average)
V43 (Home Market Value)
V377 (NumAcountTemplate Views)
+
V11 (Which
DoYouWearMostFrequent)
V13 (SendEmail)
V17 (USState)
V45 (VehicleLifeStyle)
V68 (RetailActivity)
V19 (Date)
17
Web Mining: Bayesian Nets
z Bayesian
network
4 DAG (Directed Acyclic Graph)
4 Express dependence relations between variables
4 Can use prior knowledge on the data (parameters)
A
B
C
P(A,B,C,D,E) = P(A)P(B|A)P(C|B)
P(D|A,B)P(E|B,C,D)
D
E
4 Examples of conjugate priors:
Dirichlet for multinomial data, Normal-Wishart for normal data
18
9
Web Mining: Results
z
A Bayesian net for
KDD web data
z
V229 (Order-Average) and
V240 (Friend) directly
influence V312 (Target)
z
V19 (Date) was influenced by
V240 (Friend) reflecting the
TV advertisement.
19
Summary
z
We study machine learning methods, such as
4 Probabilistic neural networks
4 Evolutionary algorithms
4 Reinforcement learning
z
Application areas include
4 Text mining
4 Web mining
4 Bioinformatics (not addressed in this talk)
z
Recent work focuses on probabilistic graphical models for
web/text/bio data mining, including
4 Bayesian networks
4 Helmholtz machines
4 Latent variable models
20
10
21
Bayesian Networks:
Architecture
L
B
G
M
P ( L, B, G , M ) = P ( L) P ( B | L) P (G | L, B ) P ( M | L, B, G )
= P ( L) P ( B ) P (G | B ) P ( M | B, L)
z
A Bayesian network represents the probabilistic
relationships between the variables.
n
P ( X) = ∏ P ( X i | pa i )
i =1
pai is the set of parent nodes of Xi.
22
11
Bayesian Networks:
Applications in IR – A Simple BN for Text Classification
C
• C: document class
• ti: ith term
t1
z
z
z
t2
t8754
The network structure represents the naïve Bayes assumption.
All nodes are binary.
[Hwang & Zhang, 2000]
23
Bayesian Networks:
Experimental Results
z
Dataset
4The acq dataset from Reuters-21578
48754 terms were selected by TFIDF.
4Training data: 8762 documents
4Test data: 3009 documents
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Parametric Learning
4Dirichlet prior assumptions for the network parameter
distributions.
p (θ ij | S h ) = Dir (θ ij | α ij1 ,..., α ijri )
4Parameter distributions are updated with training data.
p(θ ij | D, S h ) = Dir(θ ij | αij1 + Nij1 ,...,αijri + Nijri )
24
12
Bayesian Networks:
Experimental Results
For training data
z
4Accuracy: 94.28%
Recall (%)
Precision (%)
Positive examples
96.83
75.98
Negative examples
93.76
99.32
Recall (%)
Precision (%)
Positive examples
95.16
89.17
Negative examples
96.88
98.67
For test data
z
4Accuracy: 96.51%
25
Latent Variable Models:
Architecture
z
z
[Shin & Zhang, 2000]
Maximize log-likelihood
N
M
L = ∑∑ n(d n , wm ) log P(d n , wm )
n =1 m =1
N
M
K
= ∑∑ n(d n , wm ) log ∑ P( zk ) P( wm | zk ) P(d n | zk )
n =1 m =1
Document
Clustering
k =1
4Update P( zk ) , P(wm | zk ) , P(d n | zk ) .
4With EM Algorithm
Topic-Words
Extraction
Latent Variable Model for
Topic Words Extraction and Document Clustering
26
13
Latent Variable Models:
Learning
z
EM (Expectation-Maximization) Algorithm
4Algorithm to maximize pre-defined log-likelihood
z
Iteration of E-Step and M-Step
4E-Step
M-Step
N
P( zk | d n , wm )
=
P( zk ) P(d n | zk ) P( wm | zk )
K
∑ P( z ) P( d
k =1
k
n
P( wm | z k ) =
| zk ) P( wm | zk )
∑ n( d
n =1
M N
, wm ) P( z k | d n , wm )
n
∑∑ n(d
m =1 n =1
M
P(d n | z k ) =
∑ n( d
m =1
M N
n
M
, wm ) P( z k | d n , wm )
, wm ) P( z k | d n , wm )
∑∑ n(d
m =1 n =1
P( z k ) =
n
n
, wm ) P( z k | d n , wm )
1 M N
∑∑ n(d n , wm ) P( z k | d n , wm ),
R m=1 n =1
N
R ≡ ∑∑ n(d n , wm )
27
m =1 n =1
Latent Variable Models:
Applications in IR – Experimental Results
z
z
Topic Words Extraction and Document Clustering with a
subset of TREC-8 data
TREC-8 adhoc task data
4 Documents: DTDS, FR94, FT, FBIS, LATIMES
4 Topics: 401-450 (401, 434, 439, 450)
4 401: Foreign Minorities, Germany
4 434: Estonia, Economy
4 439: Inventions, Scientific discovery
4 450: King Hussein, Peace
28
14
Latent Variable Models:
Applications in IR – Experimental Results
Label (assigned to zk with Maximum P(di|zk) )
Topic (#Docs)
z2
z4
z3
z1
Precision
Recall
401 (300)
279 1
0
20
0.902
0.930
434 (347)
20
238 10
79
0.996
0.686
439 (219)
7
0
203 9
0.953
0.927
450 (293)
3
0
0
290 0.729
0.990
Topics
Extracted Topic Words (top 35 words with highest P(wj|zk)
Cluster 2
(z2 )
Cluster 4
(z4)
Cluster 3
(z3)
Cluster 1
(z1)
german, germani, mr, parti, year, foreign, people, countri, govern, asylum, polit, nation,
law, minist, europ, state, immigr, democrat, wing, social, turkish, west, east, member,
attack, …
percent, estonia, bank, state, privat, russian, year, enterprise, trade, million, trade, estonian,
econom, countri, govern, compani, foreign, baltic, polish, loan, invest, fund, product, …
research, technology, develop, mar, materi, system, nuclear, environment, electr, process,
product, power, energi, countrol, japan, pollution, structur, chemic, plant, …
jordan, peac, isreal, palestinian, king, isra, arab, meet, talk, husayn, agreem, presid, majesti,
negoti, minist, visit, region, arafat, secur, peopl, east, washington, econom, sign, relat,
jerusalem, rabin, syria, iraq, …
29
Boosting:
Algorithms
z
z
A general method of converting rough rules into a highly accurate
prediction rule
Learning procedure
4 Examine the training set
4 Derive a rough rule (weak learner)
4 Re-weight the examples in the training set, concentrating on the hard cases for
previous rules
4 Repeat T times
Importance weights
of training documents
Learner
Learner
Learner
Learner
h1
h2
h3
h4
f ( h1 , h2 , h3 , h4 )
30
15
Boosting:
Applied to Text Filtering
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Naïve Bayes
4 Traditional algorithm for text filtering
c NM =
arg max
c j ∈{ relevant , irrelevant }
P (c j ) P ( d i | c j )
n
= arg max P (c j )∏ P ( wik | c j )
cj
Assume independence
among terms
k =1
= arg max P (c j ) P ( wi1 =" our"| c j ) P ( wi 2 =" approach"| c j ) ...
cj
P ( win =" trouble"| c j )
z
Boosting naïve Bayes
4 Using naïve Bayes classifiers as weak learners
4 [Kim & Zhang, SIGIR-2000]
31
Boosting:
Applied to Text Filtering – Experimental Results
z
TREC (Text Retrieval Conference)
4 Sponsored by NIST
z
TREC-7 filtering datasets
4 Training Documents
• AP articles (1988)
• 237 MB, 79919 documents
4 Test Documents
• AP articles (1989~1990)
• 471 MB, 162999 documents
z
TREC-8 filtering datasets
4 Training Documents
• Financial Times (1991~1992)
• 167 MB, 64139 documents
4 Test Documents
• Financial Time (1993~1994)
• 382 MB, 140651 documents
4 No. of topics: 50
4 No. of topics: 50
Example of a document
32
16
Boosting:
Applied to Text Filtering – Experimental Results
Compared with the state-of-the-art text filtering systems
TREC-7
Averaged Scaled F1
Averaged Scaled F3
Boosting
ATT
NTT
PIRC
Boosting
ATT
NTT
PIRC
0.474
0.461
0.452
0.500
0.467
0.460
0.505
0.509
TREC-8
Averaged Scaled LF1
Averaged Scaled LF2
Boosting
PLT1
PLT2
PIRC
Boosting
CL
PIRC
Mer
0.717
0.712
0.713
0.714
0.722
0.721
0.734
0.720
33
Evolutionary Learning:
Applications in IR - Web-Document Retrieval
z
[Kim & Zhang, 2000]
Link Information,
HTML Tag
Information
Retrieval
<TITLE> <H> <B>
…
<A>
ww11 ww22 ww33 …
wn
w1 w2 w3 …
… wwnn
chromosomes
ww11 ww22 ww33 …
wn
w1 w2 w3 …
… wwnn
Fitness
34
17
Evolutionary Learning:
Applications in IR – Tag Weighting
z
Crossover
z
chromosome X
x1
x2
x3
Mutation
chromosome Y
… xn
y1
y2
y3
… yn
chromosome X
x1
x2
x3
change value w.p. Pm
zi = (xi + yi ) / 2 w.p. Pc
z1
z2
z3
… zn
x1
chromosome Z (offspring)
z
… xn
x2
x3
… xn
chromosome X’
Truncation selection
35
Evolutionary Learning :
Applications in IR - Experimental Results
z
Datasets
4 TREC-8 Web Track Data
4 2GB, 247491 web documents (WT2g)
4 No. of training topics: 10, No. of test topics: 10
z
Results
36
18
Reinforcement Learning:
Basic Concept
Agent
1. State st
2. Action at
Reward rt
3. Reward rt+1
Environment
4. State st+1
37
Reinforcement Learning:
Applications in IR - Information Filtering
[Seo & Zhang, 2000]
WAIR
•retrieve documents
•calculate similarity
2. Actioni
(modify profile)
Rewar
(user profile) di
1. Statei
User profile
3. Rewardi+1 (relevance
feedback)
User
4. Statei+1
Document filtering
...
Filtered documents
38
19
Reinforcement Learning:
Experimental Results (Explicit Feedback)
(%)
39
Reinforcement Learning:
Experimental Results (Implicit Feedback)
(%)
40
20