Cilfe 2003 Fin Short

8/14/2019 Cilfe 2003 Fin Short

1/23

1

Automatic

Identificationo

f

Automatic

Identificationo

f

OrganizationalS

tructu

reinWriting

OrganizationalS

tructu

reinWriting

usingMa

chineLearning

usingMachineLearning

Laurenc

eAnthonyandGeo

rgeV.

LaurenceAnthonyandGeo

rgeV.Lashkia

Lashkia

Dep

t.of

Compu

terScience,

Facul

tyof

Enginee

ring

Dep

t.of

Compu

terScience,

Facul

tyof

Enginee

ring

Okayama

Univ.of

Science,

1

Okayama

Univ.of

Science,

1--11

Ridai

Rid

ai--c

hocho,

Okayama

,Okay

ama

antho

[email protected]

p

anthony

@ice.ous.ac.j

plash

[email protected].

jp

lash

[email protected].

jp

h

ttp:

//antpc

1.ice.ou

s.ac.j

p

h

ttp:

//antpc

1.ice.ou

s.ac.j

p


2/23

2

P

resenta

tionOu

tline

P

resenta

tionOu

tline

Background

Background

ResearchAim

ResearchAim

SystemDesign(O

verview)

SystemDesign(O

verview)

ApplicationtoRes

earchAbstracts

ApplicationtoRes

earchAbstracts

Results(Accuracy

)

Results(Accuracy)

Results(EffectivenessintheClassroom)

Results(EffectivenessintheClassroo

m)

SoftwareDemons

tration

SoftwareDemons

tration

Conclusions

Conclusions


3/23

3

Background

Background

ImportanceofTextStructu

re

ImportanceofTextStructure

Swales(1981,199

0),

Swales(1981,199

0),Carrell

Carrell(1982)

(1982)

Hinds(1982,1983

),

Hinds(1982,1983

),Hoey

Hoey(1994),Winter(1994)

(1994),Winter(19

94)

StudiesonTextStructure

StudiesonTextStructure

TITL

ES

TITL

ES--Dudley

Dudley--Evans(1994),Anthony(20

01)

Evans(1994),Anthony(20

01)

ABSTRACTS

ABSTRACTS--Ay

ers(1993),P

osteguillo(19

96)

Ay

ers(1993),Posteguillo(19

96)

INSTRODUCTIO

NS

INST

RODUCTIO

NS--Swales(1990),Anthony(1999)

Swales(1990),Antho

ny(1999)

DISCUSSIONS

DISC

USSIONS--

Hopkins&Dudley

Hopkins&Dudley--Evans(

1988)

Evans(

1988)

PATENTS

PATENTS--Bazer

man(1994)

Bazer

man(1994)

GRA

NTPROPOS

ALS

GRANTPROPOS

ALS--Connor&

Connor&Mauranen

Mauranen

(1999)

(1999)

LEGALWRITING

LEGALWRITING

--Bhatia(19

93)

Bhatia(19

93)


4/23

4

Background

Background

Problems

withAnaly

zingTextS

tructure

Problems

withAnaly

zingTextS

tructure

Weneedalargecorpusoftextdata

Weneedalargecorpusoftextdata

(Thetextdatamust

(ThetextdatamustACURATELY

ACURATELYre

presentwhatw

ehopeto

re

presentwhatw

ehopeto

study)

study)

Weneedalotofresearchtime

Weneedalotofresearchtime

(Wem

ustanalyzealotoftexts)

(Wem

ustanalyzealotoftexts)

Weneedgoodvalida

tionandreliabilitytests

Weneedgoodvalida

tionandreliabilitytests

(Beca

useevaluating

structurecanbeverysubjectiv

e)

(Beca

useevaluatingstructurecanbeverysubjectiv

e)

MostTextStructure

Studiesare

MostTextStructure

Studiesare

SmallScale

SmallScale


5/23

5

Background

Background

Henryet

al.(2001)

Henryet

al.(2001)

40ApplicationLetters

40ApplicationLetters

Tarone

Taroneetal.(200

0)

etal.(200

0)

2PhysicsResearc

hArticles

2PhysicsResearc

hArticles

Connoretal.(1999)

Connoretal.(1999)

34G

rantProposa

ls

34GrantProposals

Williams(1999)

Williams(1999)

5Me

dicalResearchArticles

5Me

dicalResearchArticles

Anthony(1999)

Anthony(1999)

12ComputerScie

nceResearchArticle

12ComputerScie

nceResearchArticle

Intro

ductions

Intro

ductions


6/23

6

ResearchAim

ResearchAim

Develop

aCompu

terSystem

toProcess

Develop

aComputerSystem

toProcess

Textsan

dAnalyze

TextStructure

Textsan

dAnalyze

TextStructure

Automatically

Automatically

AAMac

hine

Learning

System

Machine

Learning

System

fortext

fortext

structu

re

structu

re

Easy

toprocessa

largecorpusoftextdata

Easy

toprocessa

largecorpus

oftextdata

FastFast

Theanalyticprocesswouldbe

clearlydefin

ed

Theanalyticprocesswouldbe

clearlydefin

ed

Easy

totestthereliabilityand

validity

Easy

totestthereliabilityand

validity


7/23

7

SystemDes

ign(Ov

erview)

SystemDes

ign(Ov

erview)

Machine

Learning:Unsupervised?

Machine

Learning:Unsuper

vised?

SupervisedLearning

Supervis

edLearning??

InSupervisedLea

rning,

InSupervisedLea

rning,

Give

thesystema

structuralm

odel(setof

classes)

Give

thesystema

structuralm

odel(setof

classes)

Give

thesysteme

xamplesofthemodel

Give

thesysteme

xamplesoft

hemodel

Tellthesystemw

hat

Tellthesystemw

hatfeatures

features

intheexam

plesare

intheexam

plesare

impo

rtant

impo

rtant

Defin

earelationbetweenthe

classesandthe

Defin

earelationbetweenthe

classesandthe

featu

res

featu

res

Class

ifynewtext

examplesby

comparingits

Class

ifynewtext

examplesby

comparingits

featu

reswiththoseineachcla

ss

featu

reswiththoseineachcla

ss


8/23

8

SystemDes

ign(Ov

erview)

SystemDes

ign(Ov

erview)

Problems

Problems

Wen

eeda

Wen

eedagood

goodmodelofstr

ucture

modelofstr

ucture

Buttherearema

nymodelsofstructureintheliterature

Buttherearema

nymodelsofstructureintheliterature

Wen

eedasetof

Wen

eedasetof

labeledexamples

labeledexamples

Butmanysystem

sworkwellwithonlyafewlabeled

Butmanysystem

sworkwellwithonlyafewlabeled

examples

examples

Wen

eeda

Wen

eedagood

goodsetoffeatures

setoffeatures

ButlanguagecontainsaLOTof

noisewords!

ButlanguagecontainsaLOTof

noisewords!

(e

.g.a,the,of,in,at,but?,though?,

(e

.g.a,the,of,in,at,but?,though?,))

Buildingalistof

featuresbyhan

disinfeasible

Buildingalistof

featuresbyhan

disinfeasible

Wen

eeda

Wen

eedagood

goodrelationbetweentheclassesand

relationbetw

eentheclassesand

thefeatures

thefeatures


9/23

9

Application

ofSystemto

Application

ofSystemto

Researc

hAbstracts

Researc

hAbstracts

Givethesystemastructuremo

del:

Givethesystemastructuremo

del:

Modified

Modified

CARSModel

(Swales,19

90:Anthony

,1999)

CARSModel

(Swales,19

90:Anthony

,1999)

Move1

Move1Establishing

Establishing

1.11.1

Claimingcentrality

Claimingcentrality

aTerritory

aTerritory

1.21.2

Makingtopicg

eneralizations

Makingtopicg

eneralizations

1.31.3

Reviewingitem

sofpreviousresearch

Reviewingitem

sofpreviousresearch

Move2

Move2Establishing

Establishing

2.1A

2.1A

Counterclamin

g

Counterclamin

g

aniche

aniche

2.1B

2.1B

Indicatingaga

p

Indicatingaga

p

2.1C

2.1C

Questionraisin

g

Questionraisin

g

2.1D

2.1D

Continuingatradition

Continuingatr

adition

Move3

Move3Occupy

ing

Occupy

ing

3.1A

3.1A

Outliningpurpose

Outliningpurpose

theniche

theniche

3.1B

3.1B

Announcingpr

esentresearch

Announcingpresentresearch

3.23.2

Announcingprincipalfindings

Announcingprincipalfindings

3.33.3

Evaluationofr

esearch

Evaluationofresearch

3.43.4

IndicatingRAstructure

IndicatingRAstructure


10/23

10

Application

ofSystemto

Application

ofSystemto

Researc

hAbstracts

Researc

hAbstracts

Givethesystemexa

mplesofth

emodel

Givethesystemexa

mplesofth

emodel

100A

bstracts(IEEETrans.onPDS)

dividedinto

100A

bstracts(IEEETrans.onPDS)

dividedinto

692labeled

692labeledStepsUnits

StepsUnits(onlyexamplesfrom6classes)

(onlyexamplesfrom6classes)

554S

tepUnits(80%

)usedfor

554S

tepUnits(80%

)usedfortrain

ing

train

ingthesystem

thesystem

138S

tepUnits(20%

)usedfor

138S

tepUnits(20%

)usedfortesting

testingthesystem

thesystem

Tellthes

ystemwha

t

Tellthesystemwhatfeatures

features

tolookat

tolookat

Allwo

rdclusters(chunks)upto5w

ordslong

Allwo

rdclusters(chunks)upto5w

ordslong

Positionofstepunitinabstract(i.e.

1

Positionofstepunitinabstract(i.e.

1ststline,2

line,2ndndline

,

line

,))

(Reduce

(ReduceNoise

NoiseinFeatures)

inFeatures)

Autom

aticallyrankw

ordsby

Autom

aticallyrankw

ordsbyimport

ance

importanceusing:

using:

rawfrequency,

rawfrequency,InformationG

ain

InformationG

ain

Useo

nlyhighranked

words

Useo

nlyhighranked

words


11/23

11

Application

ofSystemto

Application

ofSystemto

Researc

hAbstracts

Researc

hAbstracts

Inthispaper,we

propose

anewsystem.

Inthispaper,we

propose

anewsystem.

1word

chunks

1word

chunks

in/th

is/paper/w

e/propose/a/new/system

in/th

is/paper/w

e/propose/a/new/syste

m

2word

chunks

2word

chunks

inthis/thispaper

/paperwe/

wepropose/

inthis/thispaper

/paperwe/

wepropose/

proposea/anew

/newsystem

proposea/anew

/newsystem

3word

schunks

3word

schunks

inthispaper/thispaperwe/paperwepro

pose/


pose/

wep

roposea/proposeanew/anewsyste

m

wep

roposea/proposeanew/anewsyste

m


12/23

12

Application

ofSystemto

Application

ofSystemto

Researc

hAbstracts

Researc

hAbstracts

Inthispaper,we

propose

anewsystem.

Inthispaper,we

propose

anewsystem.

1word

chunks

1word

chunks

inin//th

is

th

is//paper

paper//w

e

w

e//propose

propose//aa//newnew//system

syste

m

2word

chunks

2word

chunks

inthis/

inthis/thispaper

thispaper

/paperwe/

/paperwe/

wepropose

wepropose//

proposea/anew

/

proposea/anew

/newsystem

newsystem

3word

chunks

3word

chunks

inthispaper


pose/

/thispaperwe/paperwepro

pose/

wep

roposea

wep

roposea/proposeanew/

/proposeanew/anewsyste

m

anewsyste

m


13/23

13

InformationGain

(IG)

In

formationGain

(IG)

j

j

p

p

D

Entropy

c j

2

log

)

(

1

=

where

whereppjjis

theproportionofdata(

is

theproportionofdata(DD

)inaclass

)inaclassjj

from

from

thesetofclasses

thesetofclasses

CC..

)

(

|

|

|

|

)

(

)

,(

)

(

v

v

w

Values

v

D

Entro

py

DD

D

Entropy

wD

Gain

where

where

Values

Values(w(w))isthesetofallpossiblevaluesfo

r

isthesetofallpossiblevaluesfo

r

word

wordw,

w,andand

DDvv

isthesubsetof

isthesubsetofDDfor

whichword

for

whichword

wwhashas

avalue

avaluevv..


14/23

14

InformationGain

(IG)

In

formationGain

(IG)

Process

3

10

task_

migration

2

9

difficult

1

8

not

and

7

often

is

6

transmitting

of

5

is_

often

in

4

difficult

_to

to

3

2_

however

a

2

however

the

1

InformationGain(IG)

RawFrequen

cy

Rank


15/23

15

Application

ofSystemto

Application

ofSystemto

Researc

hAbstracts

Researc

hAbstracts

Definearelationbetweenfeaturesandclasses

Definearelationbetweenfeatu

resandclasses

Usepro

babilityofea

chclassand

theprobabilityof

Useprobabilityofea

chclassand

theprobabilityof

features(clusters)beingineach

class

features

(clusters)beingineachclass

((ANA

ANAVE

BAYESClassifier)

VE

BAYESClassifier)

Class1

(ClaimingCentrality)

Class1

(ClaimingCentrality)

Class2

(Makingtopicgeneralizations)

Class2

(Makingtopicgeneralizations)

Class3

(Indicatingagap)

Class3

(Indicatingagap)

Class4

(Outliningpurpose

)

Class4

(Outliningpurpose

)

Class5

(Announcingprinc

ipalfindings)

Class5

(Announcingprinc

ipalfindings)

Class6

(Evaluationofresearch)

Class6

(Evaluationofresearch)

Class1

:

Class1Prob.

Class1

:

Class1Prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Class2

:

Class2Prob.

Class2

:

Class2Prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Class3

:

Class3Prob.

Class3

:

Class3Prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Class4

:

Class4Prob.

Class4

:

Class4Prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Class5

:

Class5Prob.

Class5

:

Class5Prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Class6

:

Class6Prob.

Class6

:

Class6Prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.

Feat.1prob.F

eat.2prob.

Feat

.3prob.


16/23

16

Application

ofSystemto

Application

ofSystemto

Researc

hAbstracts

Researc

hAbstracts

Classifythestructureofnewte

xtexample

s


xtexample

s

Choose

themostpro

bableclasscontainingthe

Choose

themostpro

bableclassc

ontainingthe

featuresineachstep

unit.

features

ineachstep

unit.

2this

paperisaneffortinthesamedirectio

n

2this

paperisaneffortinthesamedirection

(Step3

.1B

(Step3

.1B--AnnouncingP

resentResearch

AnnouncingP

resentResearch))

FeaturesContainedinTraining

Data


Data

paper(c3),

paper(c3),this_paper

this_paper(c4)

,is(c14)this(c1

8)the(c39)

(c4)

,is(c14)this(c1

8)the(c39)

2(c103)

2(c103)is_an

is_an(c364)in(c571)

(c364)in(c571)

Step1.1Pro

b.

=

Step1.1Pro

b.

=

--2.9498+

2.9498+

--7.0449+

7.0449+--7.0449+

7.0449+--4.3368+

4.3368+++--4.4058=

4.4058=--48.7690

48.7690

Step1.2Pro

b.

=

Step1.2Pro

b.

=

--1.8398+

1.8398+

--7.4899+

7.4899+--7.4899+

7.4899+--3.8523+

3.8523+++--3.8790=

3.8790=--45.5972

45.5972

Step2.1BProb.

=

Step2.1BPr

ob.

=

--3.1391+

3.1391+

--6.9157+

6.9157+--6.9157+

6.9157+--4.3507+

4.3507+++--4.2076=

4.2076=--47.0826

47.0826

Step3.1BPr

ob.

=

Step3.1BPr

ob.

=

--1.3335+

1.3335+

--4.1566+

4.1566+--4.2436+

4.2436+--4.8497+

4.8497+++--3.9169=

3.9169=--39.0836

39.0836

Step3.2Pro

b.

=

Step3.2Pro

b.

=

--1.8398+

1.8398+

--6.3677+

6.3677+--6.3677+

6.3677+--3.6936+

3.6936+++--3.7837=

3.7837=--40.8448

40.8448

Step3.3Pro

b.

=

Step3.3Pro

b.

=

--1.5809+

1.5809+

--6.6178+

6.6178+--6.6178+

6.6178+--3.7846+

3.7846+++--4.0528=

4.0528=--43.2638

43.2638

MostProbable

Step

MostProbable

Step


17/23

17

Application

ofSystemto

Application

ofSystemto

Researc

hAbstracts

Researc

hAbstracts


xtexample

s


xtexample

s

Choose

themostpro

bableclasscontainingthe

Choose

themostpro

bableclassc

ontainingthe

featuresineachstep

unit.

features

ineachstep

unit.

2this

paperisaneffortinthesamedirectio

n

2this

paperisaneffortinthesamedirection

(Step3

.1B

(Step3

.1B--AnnouncingP

resentResearch

AnnouncingP

resentResearch))


Data


Data

paper(c3),

paper(c3),this_paper

this_paper(c4)

,is(c14)this(c1

8)the(c39)

(c4)

,is(c14)this(c1

8)the(c39)

2(c103)

2(c103)is_an

is_an(c364)in(c571)

(c364)in(c571)

Step1.1Pro

b.

=

Step1.1Pro

b.

=

--2.9498+

2.9498+

--7.0449+

7.0449+--7.0449+

7.0449+--4.3368+

4.3368+++--4.4058=

4.4058=--48.7690

48.7690

Step1.2Pro

b.

=

Step1.2Pro

b.

=

--1.8398+

1.8398+

--7.4899+

7.4899+--7.4899+

7.4899+--3.8523+

3.8523+++--3.8790=

3.8790=--45.5972

45.5972

Step2.1BProb.

=

Step2.1BPr

ob.

=

--3.1391+

3.1391+

--6.9157+

6.9157+--6.9157+

6.9157+--4.3507+

4.3507+++--4.2076=

4.2076=--47.0826

47.0826

Step3.1BProb.=

Step3.1BP

rob.=

--1.3335+

1.3335+--4.1566+

4.1566+--4.2436+

4.2436+

--4.8497+

4.8497+

++--3.9169=

3.916

9=--39.0836

39.0836

Step3.2Pro

b.

=

Step3.2Pro

b.

=

--1.8398+

1.8398+

--6.3677+

6.3677+--6.3677+

6.3677+--3.6936+

3.6936+++--3.7837=

3.7837=--40.8448

40.8448

Step3.3Pro

b.

=

Step3.3Pro

b.

=

--1.5809+

1.5809+

--6.6178+

6.6178+--6.6178+

6.6178+--3.7846+

3.7846+++--4.0528=

4.0528=--43.2638

43.2638

MostProbable

Step=h

MostProbable

Step=hstep3.1B

step3.1B

==--39.0836

39.0836

(DecisionisStep3.1B

(DecisionisStep3.1BAnn

ouncingPresen

tResearch

Ann

ouncingPresen

tResearch))


18/23

18

Results(Classification

Accuracy)


Accuracy)

ClassificationAccura

cy(Overall)


cy(Overall)

554StepUnitsusedfor

554StepUnitsusedfortraining

trainingthesystem(80%o

fentiredata)

thesystem(80%o

fentiredata)

138StepUnitsusedfor

138StepUnitsusedfortesting

testingthesystem(20%o

fentiredata)

thesystem(20%o

fentiredata)

No.

ofFeatures

Accu

racy

(RawFrequency)

Accura

cy

(InformationGain)

2208(all)

56

%

-

1000

51

%

70%

700

56

%

70%

500

59

%

69%

300

59

%

69%

100

54

%

-

N

ote:Random

guessinghas

anaccuracyo

f16.66%(

NO

T50%!)

N

ote:Random

guessinghasanaccuracyof16.66%(

NO

T50%!)

Choosing

themostcom

monclass=

26%

Choosing

themostcom

monclass=

26%


19/23

19


Accuracy)


Accuracy)


cy(EachStepUnit)


cy(EachStepUnit)

Numb

eroffeatures=700

Numb

eroffeatures=

700

Rankedby

Ranke

dbyInformationGain

InformationGainmeasure

measure

Accur

acy(overall)=

70%

Accuracy(overall)=

70%

Class

Step1

.1

Step1.2

Step2.1b

Step

3.1b

Step3.2

Step3.3

Step1.1

2(43%)

4

0

0

1

0

Step1.2

0

17(77%)

0

0

4

1

Step2.1b

0

2

1(17%)

0

2

1

Step3.1b

0

0

0

34(92%)

3

0

Step3.2

0

2

0

2

25(66%)

9

Step3.3

0

1

0

2

8

17(61%)

N

ote:Classifica

tionscorrespo

ndwithCARS

Model

N

ote:Classifica

tionscorrespo

ndwithCARS

Modelmoves

moves

(Accuracy=88%wh

enusing

(Accura

cy=88%wh

enusingsec

ondopinion

sec

ondopinion

))


20/23

20

Results(In

theclas

sroom)

Results(In

theclas

sroom)

AAWindows

Windo

wsInter

face

Interface

Toenableresearchers,teache

rsandstudentsto

Toenableresearc

hers,teache

rsandstudentsto

usethesystemit

needstobe

easilyaccess

iblevia

uset

hesystemit

needstobe

easilyaccess

iblevia

aawindows

windowsinterf

ace

interf

ace

AAwindows

windowssyste

mhasbeenbuiltusingth

e

syste

mhasbeenbuiltusingth

e

programminglanguagePERL5.6andPERL

/

programminglanguagePERL5.6andPERL

/TkTk


21/23

21

Results(In

theclas

sroom)

Results(In

theclas

sroom)

Materials

Selectionb

yNon

Materials

Selectionb

yNon--NativeTeacher

NativeTeacher

Thed

ecisionsarefast.

Thed

ecisionsarefast.

Itiss

impleandeasytocompletethetask.

Itiss

impleandeasytocompletethetask.

Irely

toomuchonthesoftwareandstopfe

eling

Irely

toomuchonthesoftwareandstopfe

eling

likedo

ingtheanalysismy

self.

likedo

ingtheanalysismy

self.

Comments

Comments

1/71/7

2/72/7

Errors

Errors

28min.

28min.

(1min.foranalys

isplus

(1min.foranalys

isplus

timetocheckresults)

timetocheckresults)

100min.

100min.

Timetocompletetasks

Timetocompletetasks

UsingSystem

UsingSystem

Byhand

Byhand

Selectionof7

texts

Selectionof7

texts

from10textc

orpus

from10textc

orpus


22/23

22

Results(In

theclas

sroom)

Results(In

theclas

sroom)

TextAnalysisbyNon

TextAnalysisbyNon--NativeStudent

NativeStudent

ItItsveryfast.

sve

ryfast.

Thes

tructureisnowveryclear.

Thestructureisnowveryclear.

Thes

ystemhasclearlya

nalyzedthestructu

re,

Thesystemhasclearlya

nalyzedthestructu

re,

whaty

oushoulddoiscorrectonlythepartthatis

whaty

oushoulddoiscorrectonlythepartt

hatis

strange.Sotheworkislittle.

strange.Sotheworkislittle.

Comments

Comments

0/40/4

2/42/4

Errors

Errors

15min.

15min.

(1min.foranalys

isplus

(1min.foranalys

isplus

timetocheckresults)

timetocheckresults)

38min.

38min.

Timetocompletetasks

Timetocompletetasks

UsingSystem

UsingSystem

Byhand

Byhand

Selectionof4

texts

Selectionof4

texts

from10textc

orpus

from10textc

orpus


23/23

23

Conclusions

Conclusions

Acompu

tersystemwasdevelopedto

Acompu

tersystemwasdevelopedto

analyze

textstruc

ture

analyze

textstructure

Learningmethod:

Learningmethod:Supervised

Learning

Supervised

Learning

Accu

racy70%(8

8%w

henusingsecondopinion)

Accuracy70%(8

8%w

henusingsecondopinion)

Systemerrorscor

respondedwithCARS

SystemerrorscorrespondedwithCARS

Model

Modelm

oves

m

oves

Effective

intheclassroomforuseby

Effective

intheclassroomforuseby

teachers

andstud

ents

teachers

andstud

ents

Runsin

Windows

environm

ent

RunsinWindows

environm

ent

Documents

Cilfe 2003 Fin Short