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Full-Text Indexing

Session 10

INFM 718N

Web-Enabled Databases

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text/html; charset="us-ascii" Text Retrieval Issues

Agenda

&#= 8226;How to do it

» <= /div>

– <= /div>

&#= 8226;How it works

• <= /div>

&#= 8226;The “A” Team

------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/slide0318.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Text Retrieval Issues

Database

Server-side Programming

Interchange Language

Client-side Programming

Web Browser

Client Hardware

Server Hardware

(PC, Unix)

(MySQL)

(PHP)

(HTML, XML)

(JavaScript)

(IE, Firefox)

(PC)

Business

rules

Interaction

Design

Interface

Design

<= span style=3D'font-size:67%'>• Relational normalizatio= n

<= span style=3D'font-size:67%'>• Structured programming&= #13;

<= span style=3D'font-size:67%'>• Software patterns <= /span>

<= span style=3D'font-size:67%'>• Object-oriented design&= #13;

<= span style=3D'font-size:67%'>• Functional decompositio= n

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8YhMKpfMJqIJjUqn1Kr1is1qsc+t9wsOi8fkcrlrTqvX7Lb7rUTD5/S6/Y5fyvP8vv8PeHYQSFho eIgYtJfI2Oj4OLcIOUlZaTkleam5ydl5k+kZKjrKCEp6ippaZ6ra6voKxgo7S1urN2ibq7tbJMv7 C6zrG0xcrDpsnKzMibzs/OzYDD1NDShdjZ1Nd63d7W3G/S0+rhVOfo4OZZ7O3k607h4vrwM/b38P UI+/367P/0/uhcCBBAsaPIgwocKFDBs6fAgxosQIBQAAOw== ------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/slide0319.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Text Retrieval Issues

Full-Text Indexing in MySQL

= 226;Create a MyISAM table (not InnoDB!)

&= #8211;Include a CHAR, VARCHAR, or TEXT field

&= #8211;Text fields can hold a bit over 10,000 words

= 226;Create a FULLTEXT index

&= #8211;ALTER TABLE x ADD FULLTEXT INDEX y;

= 226;Issue a (ranked) query

–SELECT y FROM x WHERE MATCH y AGAINST (‘cat’);

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Other Types of Queries

&#= 8226;Automatic (ranked) vocabulary expansion

–SELECT y FROM x WHERE MATCH y AGAINST (‘cat’ WITH QU= ERY EXPANSION);

• <= /div>

&#= 8226;Boolean (unranked) search

= 211;SELECT y FROM x WHERE MATCH y AGAINST (‘+cat -dog’ = IN BOOLEAN MODE);

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Query Details

&#= 8226;No more than 254 characters (~40 words)

&= #8211;Longer queries take more time

• <= /div>

&#= 8226;Multiple words are implicitly joined by “OR”

• <= /div>

&#= 8226;Boolean queries can use (unnested) operators

&= #8211;Words preceded by “+” must occur (AND)

&= #8211;Words preceded by “-” must not occur (AND NOT)

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What’s a “Word?”

&#= 8226;Delimited by “white space” or “-”

&#= 8211;White-space includes space, tab, newline, …

&#= 8226;Not case sensitive

&#= 8226;Exact string match

&= #8211;No “stemming” (automatic truncation)

&#= 8226;Boolean search has additional options

&= #8211;Truncation (e.g., time*)

&= #8211;Phrases (e.g., “cats and dogs”)

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Unsearchable Words

&#= 8226;Very common words

&= #8211;Those that appear in more than 50% of docs

– <= /div>

&#= 8226;Words of 3 or fewer characters

&= #8211;Rarely are topically specific

» <= /div>

&#= 8226;Other “stopwords”

= 211;able about above according accordingly across actually after afterwards again against ain't …

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Human-Machine Synergy

&#= 8226;Machines are good at:

&= #8211;Doing simple things accurately and quickly

&= #8211;Scaling to larger collections in sublinear time

• <= /div>

&#= 8226;People are better at:

&= #8211;Accurately recognizing what they are looking for

&= #8211;Evaluating intangibles such as “quality”

– <= /div>

&#= 8226;Both are pretty bad at:

&= #8211;Mapping consistently between words and concepts

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Supporting the Search Process

Source

Selection

Query

Selection

Ranked List

Examination

Document

Delivery

Document

Query

Formulation

IR System

Query Reformulation

and

Relevance Feedback

Source

Reselection

No= minate

Choose=

Pre= dict

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Supporting the Search Process

Source

Selection

Query

Selection

Ranked List

Examination

Document

Delivery

Document

Query

Formulation

IR System

Indexing

Index

Acquisition

Collection

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Taylor’s Model of Question Formation=

Q1 Visceral = Need

Q2 Conscious= Need

Q3 Formalize= d Need

Q4 Compromis= ed Need

(Query)

End-user Search

Intermediated Search

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Search Goal

&#= 8226;Choose the same documents a human would

&= #8211;Without human intervention (less work)

&= #8211;Faster than a human could (less time)

&= #8211;As accurately as possible (less accuracy)

&#= 8226;Humans start with an information need

&= #8211;Machines start with a query

&#= 8226;Humans match documents to information needs

&#= 8211;Machines match document & query representations

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Search Component Model

Comparison Function

Representation Function

Query Formulation

Human Judgment

Representation Function<= /div>

Retrieval Status Value

Utility

Query=

Information Need

Docum= ent

Query Representation

Document Representation

Query Processing

Document Processing

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Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Text Retrieval Issues

Relevance

&#= 8226;Relevance relates a topic and a document

&= #8211;Duplicates are equally relevant, by definition

&= #8211;Constant over time and across users

&#= 8226;Pertinence relates a task and a document

&= #8211;Accounts for quality, complexity, language, …

&#= 8226;Utility relates a user and a document

&= #8211;Accounts for prior knowledge

&#= 8226;We seek utility, but relevance is what we get!

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Problems With Word Matching

&#= 8226;Word matching suffers from two problems

&= #8211;Synonymy: paper vs. article

&= #8211;Homonymy: bank (river) vs. bank (financial)

&#= 8226;Disambiguation in IR: seek to resolve homonymy

&= #8211;Index word senses rather than words

&#= 8226;Synonymy usually addressed by

&= #8211;Thesaurus-based query expansion

&= #8211;Latent semantic indexing

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“Bag of Terms” Representation

•Bag =3D a “set” that can contain dupli= cates

<= span style=3D'font-size:86%'>Ø“The quick brown fox jumped over the lazy dog’s back” ®

<= /span> {back, brown, dog, fox, jump, lazy, over, quick, the, the}

•

•Vector =3D values recorded in any consistent order=

<= span style=3D'font-size:86%'>Ø{back, brown, dog, fox, jump, lazy, over, quick= , the, the} ®

– [1 1 1 1 1 1 1 1 2]

Start 1:30

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Bag of Terms Example

The quick brown

fox jumped over

the lazy dog’s

back.

Document 1

Document 2

Now is the time

for all good men

to come to the

aid of their party.

the

quick

brown

fox

over

lazy

dog

back

now

is

time

for

all

good

men

to

come

jump

aid

of

their

party

0

0

1

1

0

1

1

0

1

1

0

0

1

0

1

0

0

1

1

0

0

1

0

0

1

0

0

1

1

0

1

0

1

1

Term

Document 1

Document 2

Stopword

     List

Start: 1:35

Questions:

What is the consistent order in this case?

Why is a consistent order needed?

How are the terms chosen in this case?

Stopword lists eliminate terms that = only convey meaning through word order

Observations:

No need to keep a position for terms that never occur

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Content-Location: file:///C:/B748DE50/718s0610_files/slide0079_image093.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhFQBNAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAUADQAJ ADkAgAAAAAAAAAJMhG+hyxCYoJy02vOwY0tfh3CelEWJaKaWAmpZ+bxhR5Ub3ZLhjNv7yIH9dKdG yjdEqT4blU04uiFruFzuGX1WqT1IbMIiTcPeoNFbAAA7 ------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/slide0079_image094.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhFQBNAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAUADAAJ ADoAgAAAAAAAAAJNRB5gp5rbHIzwRUuz3tzchSANxmGhRY6fc6puV30P6tUss5nhS5nXLtH4bqKg sTdqGVMv5RHGOg5tVCDUWuXRtMWOk5Rg4nri8gk7KgAAOw== ------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/slide0088.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Text Retrieval Issues

Boolean IR

&#= 8226;Strong points

&= #8211;Accurate, if you know the right strategies

&= #8211;Efficient for the computer

&#= 8226;Weaknesses

&= #8211;Often results in too many documents, or none

&= #8211;Users must learn Boolean logic

&= #8211;Sometimes finds relationships that don’t exist

&= #8211;Words can have many meanings

&= #8211;Choosing the right words is sometimes hard

------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/slide0086.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Text Retrieval Issues

Proximity Operators

&#= 8226;More precise versions of AND

&= #8211;“NEAR n” allows at most n-1 intervening terms

&= #8211;“WITH” requires terms to be adjacent and in order

&#= 8226;Easy to implement, but less efficient

&= #8211;Store a list of positions for each word in each doc

&= #8226;Stopwords become very important!

&= #8211;Perform normal Boolean computations

&= #8226;Treat WITH and NEAR like AND with an extra constraint

Start 2:30 after the break

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Proximity Operator Example

= 226;time AND come

&= #8211;Doc 2

&#= 8226;time (NEAR 2) come

&= #8211;Empty

&#= 8226;quick (NEAR 2) fox

&= #8211;Doc 1

= 226;quick WITH fox

&= #8211;Empty

quick

brown

fox

over

lazy

dog

back

now

time

all

good

men

come

jump

aid

their

party

0

1 (9)

Term

1 (13)

1 (6)

1 (7)

1 (8)

1 (16)

1 (1)
=
1 (2)

1 (15)

1 (4)

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1 (5)

1 (9)

1 (3)

1 (4)

1 (8)

1 (6)

1 (10)

Doc 1

Doc 2

Start 2:35

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Advantages of Ranked Retrieval

&#= 8226;Closer to the way people think

&= #8211;Some documents are better than others

• <= /div>
&#= 8226;Enriches browsing behavior

&= #8211;Decide how far down the list to go as you read it

• <= /div>
&#= 8226;Allows more flexible queries

&= #8211;Long and short queries can produce useful results

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Ranked Retrieval Challenges

&#= 8226;“Best first” is easy to say but hard to do!

&= #8211;The best we can hope for is to approximate it

• <= /div>
&#= 8226;Will the user understand the process?

&= #8211;It is hard to use a tool that you don’t understand

• <= /div>
&#= 8226;Efficiency becomes a concern

&= #8211;Only a problem for long queries, though

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Similarity-Based Queries

&#= 8226;Treat the query as if it were a document

&= #8211;Create a query bag-of-words

&#= 8226;Find the similarity of each document

&= #8211;Using the coordination measure, for example

&#= 8226;Rank order the documents by similarity

&= #8211;Most similar to the query first

&#= 8226;Surprisingly, this works pretty well!

&= #8211;Especially for very short queries

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Counting Terms

&#= 8226;Terms tell us about documents

&= #8211;If “rabbit” appears a lot, it may be about rabbits
&#= 8226;Documents tell us about terms

&= #8211;“the” is in every document -- not discriminating

= 226;Documents are most likely described well by rare terms that occur in = them frequently

&= #8211;Higher “term frequency” is stronger evidence

&#= 8211;Low “collection frequency” makes it stronger still

Contrast this with Boolean retrieval

1) TF was always 1 or zero

2) Searchers mad to consider collection frequency

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The Document Length Effect

&#= 8226;Humans look for documents with useful parts

&= #8211;But probabilities are computed for the whole

– <= /div>
&#= 8226;Document lengths vary in many collections

&= #8211;So probability calculations could be inconsistent

– <= /div>
&#= 8226;Two strategies

&= #8211;Adjust probability estimates for document length

&= #8211;Divide the documents into equal “passages”

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Incorporating Term Frequency

&#= 8226;High term frequency is evidence of meaning

&= #8211;And high IDF is evidence of term importance

&#= 8226;Recompute the bag-of-words

&= #8211;Compute TF * IDF for every element

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TF*IDF Example

4

5

6

3

1

3

1

6

5

3

4

3

7

1

nuclear<= /span>

fallout<= /span>

siberia<= /span>

contami= nated

interes= ting

complic= ated

informa= tion

retrieva= l

2

1

2

3

2

3

2

4

4

0.50

0.63

0.90

0.13

0.60

0.75

1.51

0.38

0.50

2.11

0.13

1.20

1

2

3

0.60

0.38

0.50

4

0.301

0.125

0.125

0.125

0.602

0.301

0.000

0.602

Unweighted query:

contaminated retrieval

Result: 2, 3, 1, 4

Weighted query:

contaminated(3) retrieval(1)

Result: 1, 3, 2, 4

IDF-weighted query:

contaminated retrieval

Result: 2, 3, 1, 4

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Document Length Normalization

&#= 8226;Long documents have an unfair advantage

&= #8211;They use a lot of terms

&= #8226;So they get more matches than short documents

&= #8211;And they use the same words repeatedly

&= #8226;So they have much higher term frequencies

&#= 8226;Normalization seeks to remove these effects

&= #8211;Related somehow to maximum term frequency

&= #8211;But also sensitive to the of number of terms

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“Okapi” Term Weights
TF component

IDF component

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text/html; charset="us-ascii" Text Retrieval Issues

MySQL Term Weights

<= span class=3DBB style=3D'position:absolute;left:-4.26%'>•local wei= ght =3D

• (log(tf)+1)/sumtf * U/(1+0.0115*U)

<= span class=3DBB style=3D'position:absolute;left:-4.26%'>•global we= ight =3D log((N-nf)/nf)

<= span class=3DBB style=3D'position:absolute;left:-3.59%'>•query wei= ght =3D local weight * global weight * qf

•
• tf        How many times the term appe= ars in the row
sumtf The sum of "(log(tf)+1)"= ; for all terms in the same row
U     = ;   How many unique terms are in the row
N     = ;   How many rows are in the table
nf    &nbs= p;   How many rows contain the term
qf    &nbs= p;   How many times the term appears i= n the query

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Summary

= 226;Goal: find documents most similar to the query

– <= /div>
&#= 8226;Compute normalized document term weights

&= #8211;Some combination of TF, DF, and Length

– <= /div>
&#= 8226;Optionally, get query term weights from the user

&= #8211;Estimate of term importance

– <= /div>
&#= 8226;Compute inner product of query and doc vectors

&= #8211;Multiply corresponding elements and then add

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The Indexing Process

quick

brown

fox

over

lazy

dog

back

now

time

all

good

men

come

jump

aid

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3, 5

3, 5, 7

2, 4, 6, 8

3

1, 3, 5, 7

2, 4, 8

2, 6, 8

1, 3, 5, 7, 8

6, 8

1, 3

1, 5, 7

2, 4, 6

Postings

Inverted File

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The Finished Product

quick

brown

fox

over

lazy

dog

back

now

time

all

good

men

come

jump

aid

their

party

Term

A

B

C

F

D

G

J

L

M

N

O

P

Q

T

AI

AL

BA

BR

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4, 8

2, 4, 6

1, 3, 7

1, 3, 5, 7

2, 4, 6, 8

3, 5

3, 5, 7

2, 4, 6, 8

3

1, 3, 5, 7

2, 4, 8

2, 6, 8

1, 3, 5, 7, 8

6, 8

1, 3

1, 5, 7

2, 4, 6

Postings

Inverted File

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Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Text Retrieval Issues

How Big Is the Postings File?

•Very compact for Boole= an retrieval

–About 10% of the size = of the documents

•If an aggressive stopw= ord list is used!

–<= /span>

•Not much larger for ra= nked retrieval

–Perhaps 20%

–<= /span>

•Enormous for proximity operators

–Sometimes larger than = the documents!

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Building an Inverted Index

•<= /span>Simplest solution is a single sorted array

–<= /span>Fast lookup using binary search =

–<= /span>But sorting large files on disk is very s= low

–<= /span>And adding one document means starting over

–

•<= /span>Tree structures allow easy insertion

–<= /span>But the worst case lookup time is linear

–

•<= /span>Balanced trees provide the best of both

–<= /span>Fast lookup and easy insertion <= /div>
–<= /span>But they require 45% more disk space=

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How Big is the Inverted Index?

&#= 8226;Typically smaller than the postings file

&= #8211;Depends on number of terms, not documents

– <= /div>
&#= 8226;Eventually, most terms will already be indexed

&= #8211;But the postings file will continue to grow

– <= /div>
&#= 8226;Postings dominate asymptotic space complexity

&= #8211;Linear in the number of documents

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Summary

&#= 8226;Slow indexing yields fast query processing

&= #8211;Key fact: most terms don’t appear in most documents

–
&#= 8226;We use extra disk space to save query time

&= #8211;Index space is in addition to document space

&= #8211;Time and space complexity must be balanced

» <= /div>
&#= 8226;Disk block reads are the critical resource

&= #8211;This makes index compression a big win

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Click to edit Master text styles

Second level

Third level

Fourth level

Fifth level

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Questions:

What is the consistent order in this case?

Why is a consistent order needed?

How are the terms chosen in this case?

Stopword lists eliminate terms that only convey meaning through word order

Observations:

No need to keep a position for terms that never occur

16

Start 2:30 after the break

17

Start 2:35

18

Contrast this with Boolean retrieval

1) TF was always 1 or zero

2) Searchers mad to consider collection frequency

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g_scaleHyperlinks=3D(document.all.tags("AREA").length>0) if( g_scaleHyperlinks ) InitHLinkArray() if( g_scaleInFrame||(IsWin("PPTSld") && parent.IsFullScrMode() ) ) document.body.scroll=3D"no" _RSW() if( IsWin("PPTSld") && parent.IsFullScrMode() ) FullScrInit(); =09 MakeSldVis(); ChkAutoAdv() if( runAnimations ) { if( document.all("NSPlay") ) document.all("NSPlay").autoStart =3D false; if( sld.filters && sld.filters.revealtrans ) setTimeout( "document.body.start()", sld.filters.revealtrans.duration * = 1000 ); else document.body.start(); } } function MakeSldVis()=20 { var fTrans=3Dg_showAnimation && SldHasTrans() if( fTrans )=09 { if( g_bgSound ) { idx=3Dg_bgSound.indexOf(","); pptSound.src=3Dg_bgSound.substr( 0, idx ); pptSound.loop=3D -(parseInt(g_bgSound.substr(idx+1))); } SlideObj.filters.revealtrans.Apply()=09 } SlideObj.style.visibility=3D"visible" if( fTrans ) SlideObj.filters.revealtrans.Play() } function MakeNotesVis()=20 { if( !IsNts() ) return false=20 SlideObj.style.display=3D"none" nObj =3D document.all.item("NotesObj") parent.SetHasNts(0) if( nObj ) {=20 nObj.style.display=3D"" parent.SetHasNts(1) } return 1 } function ChkAutoAdv() { if(SldHasTrans()) SlideObj.onfilterchange=3DAutoAdv else AutoAdv() } function AutoAdv() { if(!IsWin("PPTSld") || !gUseSldTimings )return var sld=3DGetCurSld() if( (sld.mAdvDelay>0) && !parent.IsFramesMode() ) setTimeout("parent.GoToNextSld()",sld.mAdvDelay) } function GetObj(id) { if(g_supportsPPTHTML) return document.all(id); else return document.getElementById(id); } function SldHasTrans() { return SlideObj.style.filter !=3D ""; } function GetSldId() { return sId=3Dlocation.href.substring(location.href.la= stIndexOf('/')+1) } function HideMenu() { if( frames["PPTSld"] && PPTSld.document.all.item("ctx= tmenu") && PPTSld.ctxtmenu.style.display!=3D"none" ) { PPTSld.ctxtmenu.styl= e.display=3D'none'; return true } return false } function IsWin( name ) { return window.name =3D=3D name } function IsNts() { return IsWin("PPTNts") } function IsSldOrNts() { return( IsWin("PPTSld")||IsWin("PPTNts") ) } function SupportsPPTAnimation() { return( navigator.platform =3D=3D "Win32"= && navigator.appVersion.indexOf("Windows")>0 ) } function SupportsPPTHTML() { var appVer=3Dnavigator.appVersion, msie=3DappVer.indexOf("MSIE "), ver=3D0 if( msie >=3D 0 ) ver=3DparseFloat( appVer.substring( msie+5, appVer.indexOf(";",msie) ) ) else ver=3DparseInt(appVer) return( ver >=3D 4 && msie >=3D 0 ) } function _RSW() { if( !g_supportsPPTHTML || IsNts() || ( !g_scaleInFrame && (!IsWin("PPTSld") || !parent.IsFullScrMode()) ) ) return var padding=3D0; if( IsWin("PPTSld") && parent.IsFramesMode() ) padding=3D6 cltWidth=3Ddocument.body.clientWidth-padding cltHeight=3Ddocument.body.clientHeight-padding factor=3D(1.0*cltWidth)/g_origW if( cltHeight < g_origH*factor ) factor=3D(1.0*cltHeight)/g_origH newSize =3D g_origSz * factor if( newSize < 1 ) newSize=3D1 s=3DSlideObj.style s.fontSize=3DnewSize+"px" s.posWidth=3Dg_origW*factor s.posHeight=3Dg_origH*factor s.posLeft=3D(cltWidth-s.posWidth+padding)/2 s.posTop=3D(cltHeight-s.posHeight+padding)/2 if( g_scaleHyperlinks ) ScaleHyperlinks( factor ) } function _InitAnimations() { animRuntimeInstalled =3D ''+document.body.localTime !=3D 'undefined'; isFullScreen =3D (window.name =3D=3D "PPTSld") && !parent.IsFramesMode(); g_animUseRuntime =3D g_showAnimation && animRuntimeInstalled && !(isFullSc= reen && parent.IsSldVisited()); if( g_animUseRuntime ) { collSeq =3D document.all.tags("seq"); if( collSeq !=3D null ) { for(ii=3D0;ii numSlds ) gSldJumpIdx =3D numSlds; if ( gSldJumpIdx >=3D 0 ) { if ( gSldJumpIdx =3D=3D 0 ) gSldJumpIdx =3D 1; var jumpTo =3D parseInt(gSldJumpIdx); gSldJump =3D 0; gSldJumpIdx =3D ""; win.GoToSld( parent.GetSldList().mList[jumpTo-1].mSldHref ) } } } function _KDH() { if( event.keyCode =3D=3D 8 ) { event.returnValue =3D 0; parent.GoToPrevSld(); } }function DocumentOnClick() { if( IsNts() || parent.HideMenu() ) return; if( ( g_allowAdvOnClick && (window.name=3D=3D"PPTSld") && !parent.IsFrames= Mode() ) || (event && event.keyCode=3D=3D32) ) { =09 if( g_animUseRuntime && g_animMainSequence && g_animMainSequence.cangonex= t ) return; parent.GoToNextSld(); } } var g_supportsPPTHTML =3D SupportsPPTHTML(), g_scaleInFrame =3D 1, gId=3D""= , g_bgSound=3D"", g_scaleHyperlinks =3D false, g_allowAdvOnClick =3D 1, g_showInBrowser = =3D 0, gLoopCont =3D 0, gUseSldTimings =3D 0; var g_showAnimation =3D g_supportsPPTHTML && SupportsPPTAnimation() && ( (w= indow.name=3D=3D"PPTSld" && !parent.IsFramesMode()) || g_showInBrowser );va= r g_animManager =3D null; var g_animUseRuntime =3D false; var g_animItemsToHide, g_animInteractiveItems, g_animSlideTime; var g_animMainSequence =3D null; var ENDSHOW_MESG=3D"End of slide show, click to exit.", SCREEN_MODE=3D"Fram= es", gIsEndShow=3D0, NUM_VIS_SLDS=3D38, SCRIPT_HREF=3D"script.js", FULLSCR_= HREF=3D"fullscreen.htm"; var gCurSld =3D gPrevSld =3D 1, g_offset =3D 0, gNtsOpen =3D gHasNts =3D gO= tlTxtExp =3D 0, gHasNarration =3D 0, gOtlOpen =3D true window.gPPTHTML=3DSupportsPPTHTML() var gMainDoc=3Dnew Array(new hrefList("slide0316.htm",1,-1,1),new hrefList(= "slide0317.htm",1,-1,1),new hrefList("slide0318.htm",1,-1,1),new hrefList("= slide0319.htm",1,-1,1),new hrefList("slide0321.htm",1,-1,1),new hrefList("s= lide0324.htm",1,-1,1),new hrefList("slide0323.htm",1,-1,1),new hrefList("sl= ide0320.htm",1,-1,1),new hrefList("slide0073.htm",1,-1,1),new hrefList("sli= de0059.htm",1,-1,1),new hrefList("slide0060.htm",1,-1,1),new hrefList("slid= e0276.htm",1,-1,1),new hrefList("slide0075.htm",1,-1,1),new hrefList("slide= 0076.htm",1,-1,1),new hrefList("slide0077.htm",1,-1,1),new hrefList("slide0= 023.htm",1,-1,1),new hrefList("slide0078.htm",1,-1,1),new hrefList("slide00= 79.htm",1,-1,1),new hrefList("slide0088.htm",1,-1,1),new hrefList("slide008= 6.htm",1,-1,1),new hrefList("slide0087.htm",1,-1,1),new hrefList("slide0090= .htm",1,-1,1),new hrefList("slide0091.htm",1,-1,1),new hrefList("slide0094.= htm",1,-1,1),new hrefList("slide0098.htm",1,-1,1),new hrefList("slide0099.h= tm",1,-1,1),new hrefList("slide0100.htm",1,-1,1),new hrefList("slide0102.ht= m",1,-1,1),new hrefList("slide0103.htm",1,-1,1),new hrefList("slide0108.htm= ",1,-1,1),new hrefList("slide0322.htm",1,-1,1),new hrefList("slide0110.htm"= ,1,-1,1),new hrefList("slide0304.htm",1,-1,1),new hrefList("slide0305.htm",= 1,-1,1),new hrefList("slide0307.htm",1,-1,1),new hrefList("slide0308.htm",1= ,-1,1),new hrefList("slide0311.htm",1,-1,1),new hrefList("slide0315.htm",1,= -1,1)); /********************************************* Frameset functions These functions control slide navigation and state of the frameset. **********************************************/ function FullScrInit() { g_allowAdvOnClick =3D GetCurSld().mAdvOnClk document.body.style.backgroundColor=3D"black" document.oncontextmenu=3Dparent._CM; document.onkeydown =3D _KDH; document.ondragstart=3DCancel document.onselectstart=3DCancel self.focus() } function Redirect( frmId ) {=09 var str=3Ddocument.location.hash,idx=3Dstr.indexOf('#'), sId=3DGetSldId() if(idx>=3D0) str=3Dstr.substr(1); if( window.name !=3D frmId && ( sId !=3D str) ) { obj =3D GetObj("Main-File") window.location.href=3Dobj.href+"#"+sId return 1 } return 0 } var MHTMLPrefix =3D CalculateMHTMLPrefix();=20 function CalculateMHTMLPrefix() { if ( document.location.protocol =3D=3D 'mhtml:') {=20 href=3Dnew String(document.location.href)=20 Start=3Dhref.indexOf('!')+1=20 End=3Dhref.lastIndexOf('/')+1=20 if (End < Start)=20 return href.substring(0, Start)=20 else=20 return href.substring(0, End)=20 } return ''; } function GetTags(base,tag) { if(g_supportsPPTHTML) return base.all.tags(tag); else return base.getElementsByTagName(tag); } function UpdNtsPane(){ if(frames["PPTNts"]) PPTNts.location.replace( MHTMLP= refix+GetHrefObj( gCurSld ).mNtsHref ) } function UpdNavPane( sldIndex ){ if(gNavLoaded) PPTNav.UpdNav() } function UpdOtNavPane(){ if(gOtlNavLoaded) PPTOtlNav.UpdOtlNav() } function UpdOtlPane(){ if(gOtlLoaded) PPTOtl.UpdOtl() } function SetHasNts( fVal ) { if( gHasNts !=3D fVal ) { gHasNts=3DfVal UpdNavPane() } } function ToggleOtlText() { gOtlTxtExp=3D!gOtlTxtExp UpdOtlPane() } function ClearMedia() { // Clear any sounds playing before launching another browser window. Other= wise, // in fullscreen mode, you'll continue to hear the sound in the frames mod= e. if (PPTSld.pptSound) PPTSld.pptSound.loop =3D 0; } function FullScreen() {=20 if ( PPTSld.g_animUseRuntime ) PPTSld.document.body.pause(); ClearMedia(); var href =3D ( document.location.protocol =3D=3D 'mhtml:') ? FULLSCR_HREF = : FULLSCR_HREF+"#"+GetHrefObj(gCurSld).mSldHref; if(PPTNav.event.ctrlKey) { var w =3D (window.screen.availWidth * 1.0) / 2.0 var h =3D w * (PPTSld.g_origH * 1.0) / PPTSld.g_origW win =3D window.open( MHTMLPrefix+href,null,"toolbar=3D0,resizable=3D1,top= =3D0,left=3D0," + "width=3D"+ w + ",height=3D" + h ); if( win.document.body && PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } else { win =3D window.open( MHTMLPrefix+href,null,"fullscreen=3Dyes" ); if( win.document.body && PPTSld.g_animUseRuntime ) win.document.body.PPTSldFrameset=3Dwindow; } } function ToggleVNarration() { rObj=3DPPTSld.document.all("NSPlay") if( rObj && !PPTSld.g_animUseRuntime ) { if( (rObj.playState =3D=3D 1)||(rObj.playState =3D=3D 0) ) rObj.Play() else if( rObj.playState =3D=3D 2 ) rObj.Pause() else return; } else if( PPTSld.g_animUseRuntime ) { narObj =3D PPTSld.document.all("narrationID") if( narObj ) narObj.togglePause() } } function GetCurSldNum() { =20 obj=3DGetHrefObj(gCurSld) if( obj.mOrigVis =3D=3D 1 ) return obj.mSldIdx else =20 return gCurSld } function GetNumSlds() { =20 if( GetHrefObj(gCurSld).mOrigVis =3D=3D 1 ) return GetSldList().mNumVisSlds; else return GetSldList().mList.length } function GetSldNum( href ) { for(ii=3D0; ii 1 ) PopSldList(); else if( !IsFramesMode() ) { if( gLoopCont ) GoToFirst() else EndShow() } } function GoToPrevSld() { ii=3DgCurSld-1 if( ii > 0 ) { obj=3DGetHrefObj(ii) while ( obj && ( obj.mVis =3D=3D 0 ) && ( ii>0 ) ) obj=3DGetHrefObj(--ii) if( ii =3D=3D 0 ) ii=3D1 GoToSldNum(ii) } } function GoToFirst(){ GoToSld( GetHrefObj(1).mSldHref ) } function GoToLast() { ii=3DGetSldList().mList.length if( ii !=3D gCurSld ) GoToSld( GetHrefObj(ii).mSldHref ) } function GoToSldNum( num ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue obj =3D GetHrefObj( num ) obj.mVis=3D1 gPrevSld=3DgCurSld gCurSld =3D num; PPTSld.location.replace(MHTMLPrefix+obj.mSldHref) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function GoToSld( href ) { if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue GetHrefObj( GetSldNum(href) ).mVis=3D1 PPTSld.location.replace(MHTMLPrefix+href) } function SldUpdated( id ) { if( id =3D=3D GetHrefObj(gCurSld).mSldHref ) return gPrevSld=3DgCurSld gCurSld=3DGetSldNum(id) if( IsFramesMode() ) { UpdNavPane(); UpdOtlPane(); UpdNtsPane() } } function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) } function HasPrevSld() { return ( gIsEndShow || ( gCurSld !=3D 1 && GetHrefO= bj( gCurSld-1 ).mVis =3D=3D 1 )||( GetCurSldNum() > 1 ) ) } function HasNextSld() { return (GetCurSldNum() !=3D GetNumSlds()) } function CloseWindow() { if( HideMenu() ) return; =09 var event =3D PPTSld.event; if( !IsFramesMode() && event && (event.keyCode=3D=3D27 || event.keyCode=3D= =3D32 || event.type=3D=3D"click" ) ) window.close( self ); CatchNumKeys( self, event ); } function Unload() { gIsEndShow=3D0; } function SetupEndShow() { gIsEndShow=3D1; PPTSld.document.body.scroll=3D"no"; PPTSld.document.onkeypress=3DCloseWindow; PPTSld.document.onclick=3DCloseWindow; PPTSld.document.oncontextmenu=3D_CM; } function EndShow() { if( IsFramesMode() ) return if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue doc=3DPPTSld.document var dir =3D doc.body.dir if( dir !=3D "rtl" ) dir =3D "ltr"; doc.open() doc.writeln('

' + ENDSHOW_MESG + '
') doc.close() } function SetSldVisited(){ GetSldList().mList[gCurSld-1].mVisited=3Dtrue } function IsSldVisited(){ return GetSldList().mList[gCurSld-1].mVisited } function hrefList( sldHref, visible, advDelay, advClk ) { this.mSldHref=3D this.mNtsHref =3D sldHref this.mOrigVis=3D this.mVis =3D visible this.mVisited=3D false this.mAdvDelay=3D advDelay this.mAdvOnClk=3D advClk } function SldList(arr,curSld,fEnd) { this.mCurSld =3D curSld; this.mList =3D new Array(); var idx =3D 1; for(ii=3D0;ii 0) { PushSldList(sldList,fEnd); gCurSld =3D 1; } else if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue } function PushSldList(arr,fEnd) { var ii =3D gSldStack.length; gSldStack[ii] =3D new SldList(arr,gCurSld,fEnd); GoToSld( gSldStack[ii].mList[0].mSldHref ); } function PopSldList() { if (gSldStack[gSldStack.length-1].fEndShow) EndShow() else { gCurSld =3D gSldStack[gSldStack.length-1].mCurSld; gSldStack[gSldStack.length-1] =3D null; gSldStack.length--; var sldList =3D gSldStack[gSldStack.length-1]; GoToSld( sldList.mList[gCurSld - 1].mSldHref ); } } var custShowList=3Dnew Array(); /********************************************* Navigation button implementation There are 2 types of buttons: ImgBtn, TxtBtn implemented as function objects. They share a similiar interface so the event handlers can call SetActive, for example, on a button=20 object without needing to know exactly=20 what type of button it is. **********************************************/ //---------------------------------- function ImgBtn( oId,bId,w,action ) //---------------------------------- { var t=3Dthis t.Perform =3D _IBP t.SetActive =3D _IBSetA t.SetInactive=3D _IBSetI t.SetPressed =3D _IBSetP t.SetDisabled=3D _IBSetD t.Enabled =3D _IBSetE t.ChangeIcon =3D null t.UserAction =3D action t.ChgState =3D _IBUI t.mObjId =3D oId t.mBorderId=3D bId t.mWidth =3D w t.mIsOn =3D t.mCurState =3D 0 } function _IBSetA() { if( this.mIsOn ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _IBSetI() { if( this.mIsOn ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0=20 } } function _IBSetP() { if( this.mIsOn ) { obj=3Dthis.ChgState( gShadowClr,gHiliteClr,2 ) obj.style.posLeft+=3D1; obj.style.posTop+=3D1 } } function _IBSetD() { =20 obj=3Dthis.ChgState( gFaceClr,gFaceClr,0 ) obj.style.posTop=3D0=20 } function _IBSetE( state ) { var t=3Dthis GetObj( t.mBorderId ).style.visibility=3D"visible" if( state !=3D t.mIsOn ) { t.mIsOn=3Dstate if( state ) t.SetInactive() else t.SetDisabled() } } function _IBP() { var t=3Dthis if( t.mIsOn ) { if( t.UserAction !=3D null ) t.UserAction() if( t.ChangeIcon ) { obj=3DGetObj(t.mObjId) if( t.ChangeIcon() ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-4)*t.mWidth else obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-0)*t.mWidth } t.SetActive() } =20 } function _IBUI( clr1,clr2,nextState ) { var t=3Dthis SetBorder( GetObj( t.mBorderId ),clr1,clr2 ) obj=3DGetObj( t.mObjId ) obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj= .style.posTop t.mCurState=3DnextState return obj } //----------------------------------------- function TxtBtn( oId,oeId,action,chkState ) //----------------------------------------- { var t=3Dthis t.Perform =3D _TBP t.SetActive =3D _TBSetA t.SetInactive=3D _TBSetI t.SetPressed =3D _TBSetP t.SetDisabled=3D _TBSetD t.SetEnabled =3D _TBSetE t.GetState =3D chkState t.UserAction =3D action t.ChgState =3D _TBUI t.mObjId =3D oId t.m_elementsId=3D oeId t.mIsOn =3D 1 } function _TBSetA() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gHiliteClr,gShadowClr,0,0 ) } function _TBSetI() { var t=3Dthis if( t.mIsOn && !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) } function _TBSetP() { if( this.mIsOn ) this.ChgState( gShadowClr,gHiliteClr,1,1 ) } function _TBSetD() { =20 this.ChgState( gFaceClr,gFaceClr,0,0 ) this.mIsOn =3D 0 } function _TBSetE() { var t=3Dthis if( !t.GetState() ) t.ChgState( gFaceClr,gFaceClr,0,0 ) else t.ChgState( gShadowClr,gHiliteClr,1,1 ) t.mIsOn =3D 1 } function _TBP() { var t=3Dthis if( t.mIsOn ) {=20 if( t.UserAction !=3D null ) t.UserAction() if( !t.GetState ) return if( t.GetState() ) t.SetPressed() else t.SetActive() } =20 } function _TBUI( clr1,clr2,lOffset,tOffset ) { SetBorder( GetObj( this.mObjId ),clr1,clr2 ) Offset( GetObj( this.m_elementsId ),lOffset,tOffset ) } function Offset( obj, top, left ){ obj.style.top=3Dtop; obj.style.left=3Dle= ft } function SetBorder( obj, upperLeft, lowerRight ) { s=3Dobj.style; s.borderStyle =3D "solid" s.borderWidth =3D 1=20 s.borderLeftColor =3D s.borderTopColor =3D upperLeft s.borderBottomColor=3D s.borderRightColor =3D lowerRight } function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] } function BtnOnOver(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetActive() } function BtnOnDown(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetPressed() } function BtnOnOut(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetInactive() } function BtnOnUp() { b=3DGetBtnObj() if( b !=3D null ) b.Perform() else Upd() } function GetNtsState(){ return parent.gNtsOpen } function GetOtlState(){ return parent.gOtlOpen } function GetOtlTxtState(){ return parent.gOtlTxtExp } function NtsBtnSetFlag( fVal ) { s=3Ddocument.all.item( this.m_flagId ).style s.display=3D"none" if( fVal ) s.display=3D"" else s.display=3D"none" } function _BSetA_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etActive() } function _BSetI_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etInactive() } function _BSetP_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S= etPressed() } function _BSetA_BorderImg() {=20 b =3D gBtnArr[this.mBorderId]=20 if( b !=3D null && this.mIsOn && !b.GetState() ) { obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 ) obj.style.posTop=3D0 } } function _BSetI_BorderImg() {=20 b =3D gBtnArr[this.mBorderId] if( b !=3D null && this.mIsOn && !b.GetState() ) { obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 ) obj.style.posTop=3D0 } } var gHiliteClr=3D"THREEDHIGHLIGHT",gShadowClr=3D"THREEDSHADOW",gFaceClr=3D"= THREEDFACE" var gBtnArr =3D new Array() gBtnArr["nb_otl"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPan= e,GetOtlState ) gBtnArr["nb_otlElem"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOt= lPane,GetOtlState ) gBtnArr["nb_nts"] =3D new ImgBtn( "nb_nts","nb_ntsBorder",10,parent.ToggleN= tsPane ) gBtnArr["nb_nts"].SetActive =3D _BSetA_BorderImg; gBtnArr["nb_nts"].SetInactive =3D _BSetI_BorderImg; gBtnArr["nb_ntsBorder"] =3D new TxtBtn( "nb_ntsBorder","nb_ntsElem",parent.= ToggleNtsPane,GetNtsState ) gBtnArr["nb_ntsElem"] =3D new TxtBtn( "nb_ntsBorder","nb_ntsElem",parent.To= ggleNtsPane,GetNtsState ) gBtnArr["nb_prevBorder"] =3D gBtnArr["nb_prev"]=3D new ImgBtn( "nb_prev","n= b_prevBorder",30,parent.GoToPrevSld ) gBtnArr["nb_nextBorder"] =3D gBtnArr["nb_next"]=3D new ImgBtn( "nb_next","n= b_nextBorder",30,parent.GoToNextSld ) gBtnArr["nb_sldshw"]=3D new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent= .FullScreen ) gBtnArr["nb_sldshwBorder"] =3D new TxtBtn( "nb_sldshw","nb_sldshwBorder",pa= rent.FullScreen,null ) gBtnArr["nb_sldshwBorder"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwBorder"].SetInactive =3D _BSetI_Border; gBtnArr["nb_sldshwText"] =3D new TxtBtn( "nb_sldshw","nb_sldshwText",parent= .FullScreen,null ) gBtnArr["nb_sldshwText"].SetActive =3D _BSetA_Border; gBtnArr["nb_sldshwText"].SetInactive =3D _BSetI_Border; gBtnArr["nb_voice"] =3D gBtnArr["nb_voiceBorder"] =3D new ImgBtn( "nb_voice= ","nb_voiceBorder",18,parent.ToggleVNarration ) gBtnArr["nb_otlTxtBorder"] =3D gBtnArr["nb_otlTxt"]=3D new ImgBtn( "nb_otlT= xt","nb_otlTxtBorder",23,parent.ToggleOtlText ) gBtnArr["nb_ntsBorder"].m_flagId=3D "nb_nts" gBtnArr["nb_ntsBorder"].SetFlag =3D NtsBtnSetFlag gBtnArr["nb_otlTxt"].ChangeIcon=3D GetOtlTxtState /********************************************* Context menu implementation _CM() is the function that's hooked up to the oncontextmenu event. Once we're asked to show the menu, we first build it by creating DIVs on-the-fly. Then we position it=20 within the screen area so it doesn't get clipped. Creating the DIVs using createElement() means we don't have to write out any extra HTML into the slide HTML files. **********************************************/ var sNext=3D"Next",sPrev=3D"Previous",sEnd=3D"End Show",sFont=3D"Arial",sAr= row=3D"Arrow",sFreeform=3D"Freeform",sRect=3D"Rectangle",sOval=3D"Oval" function ShowMenu() { BuildMenu(); var doc=3DPPTSld.document.body,x=3DPPTSld.event.clientX+doc.scrollLeft,y= =3DPPTSld.event.clientY+doc.scrollTop m =3D PPTSld.document.all.item("ctxtmenu") m.style.pixelLeft=3Dx if( (x+m.scrollWidth > doc.clientWidth)&&(x-m.scrollWidth > 0) ) m.style.pixelLeft=3Dx-m.scrollWidth m.style.pixelTop=3Dy if( (y+m.scrollHeight > doc.clientHeight)&&(y-m.scrollHeight > 0) ) m.style.pixelTop=3Dy-m.scrollHeight m.style.display=3D"" } function _CM() { if( !parent.IsFullScrMode() ) return; if(!PPTSld.event.ctrlKey) { ShowMenu() return false } else HideMenu() } function BuildMenu() { if( PPTSld.document.all.item("ctxtmenu") ) return var mObj=3DCreateItem( PPTSld.document.body ) mObj.id=3D"ctxtmenu" mObj.style.visibility=3D"hidden" var s=3DmObj.style s.position=3D"absolute" s.cursor=3D"default" s.width=3D"120px" SetCMBorder(mObj,"menu","black") var iObj=3DCreateItem( mObj ) SetCMBorder( iObj, "threedhighlight","threedshadow" ) iObj.style.padding=3D2 CreateMenuItem( iObj,sNext,M_GoNextSld,M_True ) CreateMenuItem( iObj,sPrev,M_GoPrevSld,M_HasPrevSld ) =09 CreateSeparator( iObj ) CreateMenuItem( iObj,sEnd,M_End,M_True ) mObj.style.visibility=3D"visible" } function Cancel() { window.event.cancelBubble=3Dtrue; window.event.returnVa= lue=3Dfalse } function Highlight() { ChangeClr("activecaption","threedhighlight") } function Deselect() { ChangeClr("threedface","menutext") } function Perform() { e=3DPPTSld.event.srcElement if( e.type=3D=3D"menuitem" && e.IsActive() ) e.Action() else PPTSld.event.cancelBubble=3Dtrue } function ChangeClr( bg,clr ) { e=3DPPTSld.event.srcElement if( e.type=3D=3D"menuitem" && e.IsActive() ) { e.style.backgroundColor=3Dbg e.style.color=3Dclr } } function M_HasPrevSld() { return( parent.HasPrevSld() ) } function M_GoNextSld() { if( gIsEndShow ) M_End(); else GoToNextSld() } function M_GoPrevSld() { if( gIsEndShow ) { gIsEndShow=3D0; history.back();= PPTSld.event.cancelBubble=3Dtrue; } else GoToPrevSld() } function M_True() { return true } function M_End() { window.close( self ) } function CreateMenuItem( node,text,action,eval ) { var e=3DCreateItem( node ) e.type=3D"menuitem" e.Action=3Daction e.IsActive=3Deval e.innerHTML=3Dtext if( !e.IsActive() ) e.style.color=3D"threedshadow" e.onclick=3DPerform e.onmouseover=3DHighlight e.onmouseout=3DDeselect s=3De.style; s.fontFamily=3DsFont s.fontSize=3D"9pt" s.paddingLeft=3D2 } function CreateSeparator( node ) { var sObj=3DCreateItem( node ) SetCMBorder(sObj,"menu","menu") var s=3DsObj.style s.borderTopColor=3D"threedshadow" s.borderBottomColor=3D"threedhighlight" s.height=3D1 s.fontSize=3D"0px" } function CreateItem( node ) { var elem=3DPPTSld.document.createElement("DIV") node.insertBefore( elem ) return elem } function SetCMBorder( o,ltClr,rbClr ) { var s=3Do.style s.backgroundColor=3D"menu" s.borderStyle=3D"solid" s.borderWidth=3D1 s.borderColor=3DltClr+" "+rbClr+" "+rbClr+" "+ltClr } ------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/fullscreen.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" ------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/notes_flag.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhCgAKAPcAAAAAAIAAAACAAICAAAAAgIAAgACAgICAgMwENMTExPz8/AAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAMDAwP8AAAD/AP//AAAA//8A/wD//////ywAAAAACgAKAEAIKgDx4UNA sKDAgQUTJiCogGHDhBAfFlRAMSFCiAguTozYUGJEiAk0EhQYEAA7 ------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/buttons.gif Content-Transfer-Encoding: base64 Content-Type: image/gif R0lGODlhWAESAPf4AAAAAIAAAACAAICAAAAAgIAAgACAgICAgAQEBISEBASEBISEhAQEhMTExAQE /KTM9Pz8/ERERPz8BAT8/KSkpGRkhMTcxCRkxAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA AAAAAAAAAAAAAAAAAAAAAMDAwP8AAAD/AP//AAAA//8A/wD//////yH5BAEAAPgALAAAAABYARIA QAj/APEJHEiwoMGDCBMqXMiwocOHECNKnEixosWLGCEiQKCRo8YDIEHig2DxAD4LKD1avDDhgUuV GUOGbHBR5QGYFBG4fHAB58EKECJEwCfUYAWPFGAygLCRKQIIUKM6WAohKUGgCikcFWh1INWoYKVS 7SrQ41GbIhGYLAuVQVOoC+IyXUuUAkG1HE3KFDhAwkgJc5/qDZmxsOHDiBMrXsy4sePDGyP7lChT 5MULmDPnlDw5YmW6FTNrfgi0aNGEZPEtbfo2bNTUpYeeRph69dPWrqvCNFuBIGG1A59CYPAVQtwF cw0OxWdTIOiBCQAjaAB17fOFII8voGmRc+eOkilm/z9OvTtnjN4hYk1YoT3H2rnBOphPAB9skgq3 2lcaX/5YnJVt9BloAar12UEIRGCZdQghMABI1Tn32IQUVthYexhmqGGGMBHAgIfEhSiiiATAtOGJ GnYI4ogsEldicAYOGNJGZaml3Y0L3NTZUBz1xlB0PUoUI0jlVWRSVN89xBKSGB1YU1TMoRfVBEle hR9HQs1GEHxQ3RZZbveZthxt/LEmXHyp5XWABSCpaRJwbM13gAACPHVccgJFgF+NEhqUAEkQSCfc ddhVhh5dVTaEAEsTUNlkZUWKN9BNGDE6QU8OrSdbllzpxyVTTrU21Uj3scfhp7nNpyoDaTIEJ3P9 If8J2kbLNYegg9JRBwGhFvbq66+ZUiDssMQWK2yHxiZLrInKNotss8l+B5yBrr5p2UIJvortANoC 6+234IbLla/elRtZjeamh5CC3Sb0YLvixivvYkDFGlZqBNh7r4n6goVvv6/ZlCicN+kL71ALTnpQ dCIZp7CihkLA3UQmoWRBogyxtBPGCTnZncVR1rSTo5nuydC/Eo8kUH+lOoTyyvjoCuZu+PjYIKLD rZybtrM1d10C0QXKEVz48GrQkEeWdFJKlbb0UmEeV0SzyC6RvNB646LWIVjUNeC1zAFb+RDKXksM 9r531bwmpQW9+hQDDsx5pqwDRWBXcFES2hc+gEn/wJGdEEWdE6LoWcoxQweaTNmkhydkKaYMYY2Q p1unbLl/pPKbn3v7EZRvrKvqlja2ONvrtoIw9tn2g32/tQBlhtYkU+MNikZ7QYlPDPuMldpOmuJX nVo5VF3HjI+q89mteX5IDd/fVFClma5kiE4f2XUJhlz09gj2NbTDRifkmu7zXjR++RVJ/lPznuvr QH2iD6S+UewP9Dnon6d2NLwN8n90BB6hC68cFMCHoe+ACASP9c4DowUyEF0OPFcDI0ij/VXPaAKC UfhS95AKGoQ7IBRICI1HwhGaUIQoLGEKT6jCFrLwhSuMoQtlCMMZ2rCGOKShDm+4wxzy8Ic+DGIP B4cIRCKGMCAAOy== ------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/frame.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii" Text Retrieval Issues ------=_NextPart_01C65910.A17ED330 Content-Location: file:///C:/B748DE50/718s0610_files/outline.htm Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset="us-ascii"

No= tes

Slide Show

Outline

1

Full-Text Indexi= ng

Session 10

INFM 718N

Web-Enabled Databases

2

Agenda

How to do it

How it works

The “A” Team

3

4

Full-Text Indexi= ng in MySQL

Create a MyISAM table (not InnoDB!)

Include a CHAR, VARCHAR, or TEXT field

Text fields can hold a bit over 10,000 words

Create a FULLTEXT index

ALTER TABLE x ADD FULLTEXT INDEX y;

Issue a (ranked) query

SELECT y FROM x WHERE MATCH y AGAINST (‘cat’);

5

Other Types of Q= ueries

Automatic (ranked) vocabulary expansion

SELECT y FROM x WHERE MATCH y AGAINST (‘cat’ WITH QUERY EXPANSION);

Boolean (unranked) search

SELECT y FROM x WHERE MATCH y AGAINST (‘+cat -dog’ IN BOOLEAN MODE);

6

Query Details

No more than 254 characters (~40 words)

Longer queries take more time

Multiple words are implicitly joined by “OR”

Boolean queries can use (unnested) operators

Words preceded by “+” must occur (AND)

Words preceded by “-” must not occur (AND NOT)

7

What’s a “Word?”

Delimited by “white space” or “-”

White-space includes space, tab, newline, …

Not case sensitive

Exact string match

No “stemming” (automatic truncation)

Boolean search has additional options

Truncation (e.g., time*)

Phrases (e.g., “cats and dogs”)

8

Unsearchable Wor= ds

Very common words

Those that appear in more than 50% of docs

Words of 3 or fewer characters

Rarely are topically specific

Other “stopwords”

able about above according accordingly across actually after afterw= ards again against ain't …

9

Human-Machine Sy= nergy

Machines are good at:

Doing simple things accurately and quickly

Scaling to larger collections in sublinear time

People are better at:

Accurately recognizing what they are looking for

Evaluating intangibles such as “quality”

Both are pretty bad at:

Mapping consistently between words and concepts

10

Supporting the = Search Process

11

Supporting the = Search Process

12

13

Search Goal=

Choose the same documents a human would

Without human intervention (less work)

Faster than a human could (less time)

As accurately as possible (less accuracy)

Humans start with an information need

Machines start with a query

Humans match documents to information needs

Machines match document & query representations

14

Search Componen= t Model

15

Relevance

Relevance relates a topic and a document

Duplicates are equally relevant, by definition

Constant over time and across users

Pertinence relates a task and a document

Accounts for quality, complexity, language, …

Utility relates a user and a document

Accounts for prior knowledge

We seek utility, but relevance is what we get!

16

Problems With W= ord Matching

Word matching suffers from two problems

Synonymy: paper vs. article

Homonymy: bank (river) vs. bank (financial)

Disambiguation in IR: seek to resolve homonymy

Index word senses rather than words

Synonymy usually addressed by

Thesaurus-based query expansion

Latent semantic indexing

17

“Bag of Terms” Representation

Bag =3D a “set” that can contain duplicates

“The quick brown fox jumped over the lazy dog’s back= 221; ®

&nbs= p; {back, brown, dog, fox, jump, lazy, over, quick, the, the}<= /font>

Vector =3D values recorded in any consistent order

{back, brown, dog, fox, jump, lazy, over, quick, the, the} ®

[1 1 1 1 1 1 1 1 2]

18

Bag of Terms Ex= ample

19

Boolean IR<= /font>

Strong points

Accurate, if you know the right strategies

Efficient for the computer

Weaknesses

Often results in too many documents, or none

Users must learn Boolean logic

Sometimes finds relationships that don’t exist

Words can have many meanings

Choosing the right words is sometimes hard

20

Proximity Opera= tors

More precise versions of AND

“NEAR n” allows at most n-1 intervening terms

“WITH” requires terms to be adjacent and in order

Easy to implement, but less efficient

Store a list of positions for each word in each doc

Stopwords become very important!

Perform normal Boolean computations

Treat WITH and NEAR like AND with an extra constraint

21

Proximity Opera= tor Example

time AND come

Doc 2

time (NEAR 2) come

Empty

quick (NEAR 2) fox

Doc 1

quick WITH fox

Empty

22

Advantages of R= anked Retrieval

Closer to the way people think

Some documents are better than others

Enriches browsing behavior

Decide how far down the list to go as you read it

Allows more flexible queries

Long and short queries can produce useful results

23

Ranked Retrieval Challenges

“Best first” is easy to say but hard to do!

The best we can hope for is to approximate it

Will the user understand the process?

It is hard to use a tool that you don’t understand

Efficiency becomes a concern

Only a problem for long queries, though

24

Similarity-Based Queries

Treat the query as if it were a document

Create a query bag-of-words

Find the similarity of each document

Using the coordination measure, for example

Rank order the documents by similarity

Most similar to the query first

Surprisingly, this works pretty well!

Especially for very short queries

25

Counting Terms<= /a>

Terms tell us about documents

If “rabbit” appears a lot, it may be about rabbits

Documents tell us about terms

“the” is in every document -- not discriminating

Documents are most likely described well by rare terms that occur in them frequently

Higher “term frequency” is stronger evidence

Low “collection frequency” makes it stronger still

26

The Document Le= ngth Effect

Humans look for documents with useful parts

But probabilities are computed for the whole

Document lengths vary in many collections

So probability calculations could be inconsistent

Two strategies

Adjust probability estimates for document length

Divide the documents into equal “passages”

27

Incorporating T= erm Frequency

High term frequency is evidence of meaning

And high IDF is evidence of term importance

Recompute the bag-of-words

Compute TF * IDF for every element

28

TF*IDF Example<= /a>

29

Document Length Normalization

Long documents have an unfair advantage

They use a lot of terms

So they get more matches than short documents

And they use the same words repeatedly

So they have much higher term frequencies

Normalization seeks to remove these effects

Related somehow to maximum term frequency

But also sensitive to the of number of terms

30

“Okapi= 221; Term Weights

31

MySQL Term Weig= hts

local weight =3D

(log(tf)+1)/sumtf * U/(1+0.0115*U)

global weight =3D log((N-nf)/nf)

query weight =3D local weight * global weight * qf

tf      = How many times the term appears in the row
sumtf The sum of "(log(tf)+1)" for all terms in the same row
U        How many unique t= erms are in the row
N        How many rows are= in the table
nf        How many rows contain the term
qf        How many times the term app= ears in the query

32

Summary

Goal: find documents most similar to the query

Compute normalized document term weights

Some combination of TF, DF, and Length

Optionally, get query term weights from the user

Estimate of term importance

Compute inner product of query and doc vectors

Multiply corresponding elements and then add

33

The Indexing Pr= ocess

34

The Finished Pr= oduct

35

How Big Is the = Postings File?

Very compact for Boolean retrieval

About 10% of the size of the documents

If an aggressive stopword list is used!

Not much larger for ranked retrieval

Perhaps 20%

Enormous for proximity operators

Sometimes larger than the documents!

36

Building an Inv= erted Index

Simplest solution is a single sorted array

Fast lookup using binary search

But sorting large files on disk is very slow

And adding one document means starting over

Tree structures allow easy insertion

But the worst case lookup time is linear

Balanced trees provide the best of both

Fast lookup and easy insertion

But they require 45% more disk space

37

How Big is the = Inverted Index?

Typically smaller than the postings file

Depends on number of terms, not documents

Eventually, most terms will already be indexed

But the postings file will continue to grow

Postings dominate asymptotic space complexity

Linear in the number of documents

38

Summary

Slow indexing yields fast query processing

Key fact: most terms don’t appear in most documents

We use extra disk space to save query time

Index space is in addition to document space

Time and space complexity must be balanced

Disk block reads are the critical resource

This makes index compression a big win

<= /a>= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
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	Start: 1:35

	Questions:

	What is the consistent order in this case?
	Why is a consistent order needed?

	How are the terms chosen in this case?
	Stopword lists eliminate terms that = only convey meaning through word order

	Observations:

	No need to keep a position for terms that never occur


	Contrast this with Boolean retrieval

	1) TF was always 1 or zero
	2) Searchers mad to consider collection frequency

1	Full-Text Indexi= ng Session 10 INFM 718N Web-Enabled Databases
2	Agenda How to do it How it works The “A” Team
3
4	Full-Text Indexi= ng in MySQL Create a MyISAM table (not InnoDB!) Include a CHAR, VARCHAR, or TEXT field Text fields can hold a bit over 10,000 words Create a FULLTEXT index ALTER TABLE x ADD FULLTEXT INDEX y; Issue a (ranked) query SELECT y FROM x WHERE MATCH y AGAINST (‘cat’);
5	Other Types of Q= ueries Automatic (ranked) vocabulary expansion SELECT y FROM x WHERE MATCH y AGAINST (‘cat’ WITH QUERY EXPANSION); Boolean (unranked) search SELECT y FROM x WHERE MATCH y AGAINST (‘+cat -dog’ IN BOOLEAN MODE);
6	Query Details No more than 254 characters (~40 words) Longer queries take more time Multiple words are implicitly joined by “OR” Boolean queries can use (unnested) operators Words preceded by “+” must occur (AND) Words preceded by “-” must not occur (AND NOT)
7	What’s a “Word?” Delimited by “white space” or “-” White-space includes space, tab, newline, … Not case sensitive Exact string match No “stemming” (automatic truncation) Boolean search has additional options Truncation (e.g., time*) Phrases (e.g., “cats and dogs”)
8	Unsearchable Wor= ds Very common words Those that appear in more than 50% of docs Words of 3 or fewer characters Rarely are topically specific Other “stopwords” able about above according accordingly across actually after afterw= ards again against ain't …
9	Human-Machine Sy= nergy Machines are good at: Doing simple things accurately and quickly Scaling to larger collections in sublinear time People are better at: Accurately recognizing what they are looking for Evaluating intangibles such as “quality” Both are pretty bad at: Mapping consistently between words and concepts
10	Supporting the = Search Process
11	Supporting the = Search Process
12
13	Search Goal= Choose the same documents a human would Without human intervention (less work) Faster than a human could (less time) As accurately as possible (less accuracy) Humans start with an information need Machines start with a query Humans match documents to information needs Machines match document & query representations
14	Search Componen= t Model
15	Relevance Relevance relates a topic and a document Duplicates are equally relevant, by definition Constant over time and across users Pertinence relates a task and a document Accounts for quality, complexity, language, … Utility relates a user and a document Accounts for prior knowledge We seek utility, but relevance is what we get!
16	Problems With W= ord Matching Word matching suffers from two problems Synonymy: paper vs. article Homonymy: bank (river) vs. bank (financial) Disambiguation in IR: seek to resolve homonymy Index word senses rather than words Synonymy usually addressed by Thesaurus-based query expansion Latent semantic indexing
17	“Bag of Terms” Representation Bag =3D a “set” that can contain duplicates “The quick brown fox jumped over the lazy dog’s back= 221; ® &nbs= p; {back, brown, dog, fox, jump, lazy, over, quick, the, the}<= /font> Vector =3D values recorded in any consistent order {back, brown, dog, fox, jump, lazy, over, quick, the, the} ® [1 1 1 1 1 1 1 1 2]
18	Bag of Terms Ex= ample
19	Boolean IR<= /font> Strong points Accurate, if you know the right strategies Efficient for the computer Weaknesses Often results in too many documents, or none Users must learn Boolean logic Sometimes finds relationships that don’t exist Words can have many meanings Choosing the right words is sometimes hard
20	Proximity Opera= tors More precise versions of AND “NEAR n” allows at most n-1 intervening terms “WITH” requires terms to be adjacent and in order Easy to implement, but less efficient Store a list of positions for each word in each doc Stopwords become very important! Perform normal Boolean computations Treat WITH and NEAR like AND with an extra constraint
21	Proximity Opera= tor Example time AND come Doc 2 time (NEAR 2) come Empty quick (NEAR 2) fox Doc 1 quick WITH fox Empty
22	Advantages of R= anked Retrieval Closer to the way people think Some documents are better than others Enriches browsing behavior Decide how far down the list to go as you read it Allows more flexible queries Long and short queries can produce useful results
23	Ranked Retrieval Challenges “Best first” is easy to say but hard to do! The best we can hope for is to approximate it Will the user understand the process? It is hard to use a tool that you don’t understand Efficiency becomes a concern Only a problem for long queries, though
24	Similarity-Based Queries Treat the query as if it were a document Create a query bag-of-words Find the similarity of each document Using the coordination measure, for example Rank order the documents by similarity Most similar to the query first Surprisingly, this works pretty well! Especially for very short queries
25	Counting Terms<= /a> Terms tell us about documents If “rabbit” appears a lot, it may be about rabbits Documents tell us about terms “the” is in every document -- not discriminating Documents are most likely described well by rare terms that occur in them frequently Higher “term frequency” is stronger evidence Low “collection frequency” makes it stronger still
26	The Document Le= ngth Effect Humans look for documents with useful parts But probabilities are computed for the whole Document lengths vary in many collections So probability calculations could be inconsistent Two strategies Adjust probability estimates for document length Divide the documents into equal “passages”
27	Incorporating T= erm Frequency High term frequency is evidence of meaning And high IDF is evidence of term importance Recompute the bag-of-words Compute TF * IDF for every element
28	TF*IDF Example<= /a>
29	Document Length Normalization Long documents have an unfair advantage They use a lot of terms So they get more matches than short documents And they use the same words repeatedly So they have much higher term frequencies Normalization seeks to remove these effects Related somehow to maximum term frequency But also sensitive to the of number of terms
30	“Okapi= 221; Term Weights
31	MySQL Term Weig= hts local weight =3D (log(tf)+1)/sumtf * U/(1+0.0115U) global weight =3D log((N-nf)/nf) query weight =3D local weight global weight * qf tf = How many times the term appears in the row sumtf The sum of "(log(tf)+1)" for all terms in the same row U How many unique t= erms are in the row N How many rows are= in the table nf How many rows contain the term qf How many times the term app= ears in the query
32	Summary Goal: find documents most similar to the query Compute normalized document term weights Some combination of TF, DF, and Length Optionally, get query term weights from the user Estimate of term importance Compute inner product of query and doc vectors Multiply corresponding elements and then add
33	The Indexing Pr= ocess
34	The Finished Pr= oduct
35	How Big Is the = Postings File? Very compact for Boolean retrieval About 10% of the size of the documents If an aggressive stopword list is used! Not much larger for ranked retrieval Perhaps 20% Enormous for proximity operators Sometimes larger than the documents!
36	Building an Inv= erted Index Simplest solution is a single sorted array Fast lookup using binary search But sorting large files on disk is very slow And adding one document means starting over Tree structures allow easy insertion But the worst case lookup time is linear Balanced trees provide the best of both Fast lookup and easy insertion But they require 45% more disk space
37	How Big is the = Inverted Index? Typically smaller than the postings file Depends on number of terms, not documents Eventually, most terms will already be indexed But the postings file will continue to grow Postings dominate asymptotic space complexity Linear in the number of documents
38	Summary Slow indexing yields fast query processing Key fact: most terms don’t appear in most documents We use extra disk space to save query time Index space is in addition to document space Time and space complexity must be balanced Disk block reads are the critical resource This makes index compression a big win