資訊檢索導論 introduction to information retrieval

8/2/2019 Introduction to information retrieval

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90

Web

Web

2004 Pew Fallows 2004

92%

Web

1990

Web Web

Web

SanfordStuttgart


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II

7590

8

1

2

3

4

5

15 Boolean retrieval

6 7

8

8 921 9

10

XML HTML

6 11

12 11

12

1318 1315

13

14 6

Rocchio kNN


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III

k nearest neighbor

15

1618 16

K EM

17

18

1921 Web 19 Web

Web 20

21 Web

Cross-language IRGrossman and Frieder 2004 4 Oard

and Dorr 1996

Image and multimedia IRGrossman and Frieder 2004

4 Baeza-Yates and Ribeiro-Neto 1999 611 12 del Bimbo 1999Lew

2001Smeulders et al.2000

Speech retrievalCoden et al.2002

Music retrievalDownie 2006 http://www.ismir.net/

User interfaces for IRBaeza-Yates and Ribeiro-

Neto 1999 10

Parallel and peer-to-peer IR P2P Grossman and Frieder 2004

7 Baeza-Yates and Ribeiro-Neto 1999 9 Aberer 2001

Digital librariesBaeza-Yates and Ribeiro-Neto 1999 15

Lesk 2004

Information science perspectiveKorfhage1997Meadow et al.1999Ingwersen and Jarvelin 2005


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IV

Logic-based approaches to IRvan Rijsbergen 1989

Natural language processing techniquesManning and

Schutze 1999Jurafsky and Martin 2008Lewis and Jones 1996

21

15 6 7

810 11

11.1 1113 15

18

18.1 21

[*][**][***]

Lauren Cowles

Cheryl AasheimJosh AttenbergLuc BelangerTom BreuelDaniel Burckhardt

Georg BuscherFazli CanDinquan ChenErnest DavisPedro DomingosRodrigo Panchiniak

FernandesPaolo Ferragina Norbert FuhrVignesh GanapathyElmer GardunoXiubo Geng

David GondekSergio GovoniCorinna HabetsBen HandyDonna HarmanBenjamin Haskell

Thomas HuhnDeepak JainRalf JankowitschDinakar JayarajanVinay KakadeMei Kobayashi

Wessel KraaijRick LafleurFlorian LawsHang LiDavid MannEnnio MasiFrank McCown

Paul McNameeSven Meyer zu EissenAlexander MurzakuGonzalo NavarroScott Olsson

Daniel Paiva

Tao Qin

Megha Raghava

Ghulam Raza

Michal Rosen-Zvi

Klaus Rothenhausler

Kenyu L. RunnerAlexander SalamancaGrigory SapunovTobias SchefferNico Schlaefer


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V

Evgeny ShadchnevIan SoboroffBenno SteinMarcin SydowAndrew TurnerJason UttHuey

VoTravis WadeMike WalshChangliang WangRenjing Wang Thomas Zeume

James AllanOmar AlonsoIsmail Sengor AltingovdeVo NgocAnhRoi

BlancoEric BreckEric BrownMark CarmanCarlos CastilloJunghoo ChoAron CulottaDoug

CuttingMeghana DeodharSusan DumaisJohannes FurnkranzAndreas HesDjoerd Hiemstra

David HullThorsten JoachimsSiddharth Jonathan J. B.Jaap KampsMounia LalmasAmy

LangvilleNicholas LesterDave LewisStephen LiuDaniel LowdYosi MassJeff Michels

Alessandro MoschittiAmir NajmiMarc NajorkGiorgio Maria Di NunzioPaul OgilviePriyank

PatelJan PedersenKathryn PedingsVassilis PlachourasDaniel RamageStefan RiezlerMichael

SchiehlenHelmut SchmidFalk Nicolas ScholerSabine Schulte im WaldeFabrizio Sebastiani

Sarabjeet SinghAlexander StrehlJohn TaitShivakumar VaithyanathanEllen VoorheesGerhard

WeikumDawid WeissYiming YangYisong YueJian Zhang Justin Zobel

Pavel BerkhinStefan ButtcherJamie CallanByron DomTorsten Suel Andrew Trotman

1314 15 Ray Mooney

Ray Mooney 3

Ray Mooney

C. D. Manning

P. Raghavan Yahoo!

H. Schutze

http://informationretrieval.org

Email [email protected]


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Snippet XML

Prabhakar Raghavan

[email protected]

http://ir.ict.ac.cn/~wangbin/iir-book/

http://nlp.stanford.edu/IR-book/

information-retrieval-book.html


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1.1 4

1.2 8

1.3 11

1.4 15

1.5 18

21

2.1 22

2.1.1 22

2.1.2 23

2.2 25

2.2.1 25

2.2.2 30

2.2.3 31

2.2.4 35

2.3 39

2.4 41

2.4.1 42

2.4.2 43

2.4.3 46

2.5 48

11

22


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51

3.1 52

3.2 55

3.2.1 56

3.2.2 k-gram 57

3.3 59

3.3.1 59

3.3.2 60

3.3.3 61

3.3.4 k-gram 63

3.3.5 64

3.4 66

3.5 67

69

4.1 70

4.2 72

4.3 75

4.4 77

4.5 80

4.6 83

4.7 86

89

5.1 91

5.1.1 Heaps 93

5.1.2 Zipf 94

5.2 95

5.2.1 96

5.2.2 97

33

44

55


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5.3 100

5.3.1 101

5.3.2 103

5.4 111

115

6.1 116

6.1.1 118

6.1.2 120

6.1.3 g 122

6.2 123

6.2.1 124

6.2.2 tf-idf 125

6.3 126

6.3.1 127

6.3.2 130

6.3.3

1316.4 tf-idf 133

6.4.1 tf 133

6.4.2 tf 133

6.4.3 134

6.4.4 135

6.5 139

141

7.1 142

7.1.1 K 143

7.1.2 144

7.1.3 145

7.1.4 145

7.1.5 147

66

77


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7.1.6 147

7.2 149

7.2.1 150

7.2.2 150

7.2.3 151

7.2.4 152

7.3 153

7.3.1 154

7.3.2 154

7.3.3 155

7.4 155

157

8.1 158

8.2 160

8.3 161

8.4

1658.5 171

8.6 175

8.6.1 175

8.6.2 176

8.6.3 177

8.7 177

8.8 180

183

9.1 185

9.1.1 Rocchio 188

9.1.2 190

9.1.3 191

9.1.4 Web 193

88

99


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9.1.5 193

9.1.6 194

9.1.7 195

9.1.8 195

9.2 196

9.2.1 196

9.2.2 196

9.2.3 198

9.3 200

XML 203

10.1 XML 206

10.2 XML 210

10.3 XML 215

10.4 XML 219

10.5 XML 223

10.6

225

229

11.1 230

11.2 232

11.2.1 1/0 232

11.2.2 233

11.3 233

11.3.1 235

11.3.2 237

11.3.3 239

11.3.4 240

11.4 242

11.4.1 242

11.4.2 243

1010

1111


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11.4.3 Okapi BM25 244

11.4.4 IR 246

11.5 247

249

12.1 250

121.1 250

12.1.2 253

12.1.3 254

12.2 255

12.2.1 IR 255

12.2.2 256

12.2.3 Ponte Croft 259

12.3 262

12.4 LM 263

12.5 265

267

13.1 271

13.2 273

13.3 278

13.4 NB 280

13.5 286

13.5.1 287

13.5.2 2 290

13.5.3 292

13.5.4 292

13.5.5 293

13.6 294

13.7 300

1212

1313


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303

14.1 305

14.2 Rocchio 307

14.3 k 311

14.4 316

14.5 321

14.6 323

14.7 330

333

15.1 334

15.2 341

15.2.1 341

15.2.2 343

15.2.3 344

15.2.4 347

15.3 348

15.3.1 349

15.3.2 351

15.4 ad hoc 355

15.4.1 355

15.4.2 357

15.5 359

363

16.1 365

16.2 368

16.3 370

16.4 K- 374

16.5 382

1414

1515

1616


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16.6 387

391

17.1 393

17.2 396

17.3 403

17.4 405

17.5 407

17.6 409

17.7 410

17.8 412

17.9 414

417

18.1 418

18.2 - SVD 42218.3 424

18.4 LSI 427

18.5 432

Web 433

19.1 434

19.2 Web 436

19.2.1 Web 438

19.2.2 439

19.3 441

19.4 444

19.5 446

19.6 shingling 449

19.7 454

1717

1818

1919


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Web 455

20.1 456

20.1.1 456

20.1.2 457

20.2 457

20.2.1 458

20.2.2 DNS 462

20.2.3 URL 463

20.3 466

20.4 467

20.5 470

473

21.1 Web 474

21.2 PageRank 476

21.2.1 478

21.2.2 PageRank 480

21.2.3 PageRank 483

21.3 Hub Authority 486

21.4 492

495

531

2020

2121


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Information Retrieval IR

Web

unstructured data

structured

data

semistructured data

Java threading

clustering

information retrieval retrieval information retrieval

information

retrieval

search information retrievalsearch

information retrieval


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classification

Web web search

Web

personal information

retrieval

MacOS X Spotlight Windows Vista

domain-specific search

Web


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4

. .

. .

.

Shakespeares Collected Works

Brutus Caesar Calpurnia

Brutus

Caesar Calpurnia

grepping Unix

grep grepping

regular expression

00

()

() grep

Romans NEAR countrymen NEAR

()

index

,000

BrutusCaesar Calpurnia Brutus Marcus Brutus

Caesar

Julius Caesar Calpurnia Calpurnia

Pisonis


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incidence matrix -term

. word

I-Hong Kong

- - td (t, d) 0

Brutus AND Caesar AND NOT Calpurnia

BrutusCaesar Calpurnia Calpurnia

complement AND

000 AND 0 AND 0 = 0000

Antony

and CleopatraHamlet -

term

Antony and Cleopatra

Julius CaesarThe TempestHamlet

OthelloMacbethAntony

Cleopetra

Antonyand

Cleopatra

JuliusCaesar

TheTempest

Hamlet Othello Macbeth ...

Antony 0 0 0 Brutus 0 0 0Caesar 0 Caplurnia 0 0 0 0 0Cleopatra 0 0 0 0 0mercy 0 worser 0 0...


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pipeline leaks

pipeline rupture

effectiveness

precision

recall

-

0 00

- 000 0 00

0

000 00

-0 0 000000000 -

.%-0 /000 0

inver ted

index

-

dictionary vocabulary lexicon

dictionary - vocabulary

0 000

dictionary - vocabulary


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8

posting

posting listinverted list

postings -

ID .

..

-

.

()

Friends, Romans, countrymen. So let it be with Caesar ...

() token

tokenization

Friends Romans countrymen So ...

() Friend roman countrymen So ...

()

ID ID

ID

token

token

Brutus

Caesar

Calpurnia

0


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.

sort-based indexing

docID

ID

-

-

-

document frequency

docID

ad hoc

disk

singly linked list

. skip

listvariable length array

Unix Unix sort uniq


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- ID

ID

term frequency

doc ID doc ID

I did enact Julius Caesar: I was

killed i' the Capitol; Brutus killed

me.

So let it be with Caeser. The noble

Brutus hath told you Caesar was

ambitious:

I

did

enatc

julius

caesar

I

was

killed

i'

the

capitol

burtus

killed

me

so

let

it

be

with

caesarthe

noble

brutus

hath

told

you

caesar

was

ambitious

I

I

i'

ambitious

be

brutus

brutus

capitol

caesar

caesar

caesardid

enact

hath

it

julius

killed

killed

let

menoble

so

the

the

told

you

was

was

with

with

was

you

told

the

so

noble

me

let

killed

julius

it

ambitious

be

brutus

capitol

caesar

did

enact

hath

I

i'


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cache

offset

traverse

-

disk seek

1-1 [*] 1-3

new home sales top forecasts

home sales rise in july

increase in home sales in july

july new home sales rise

1-2 [*]

breakthrough drug for schizophrenia

new schizophrenia drug

new approach for treatment of schizophrenia

new hopes for schizophrenia patients

a.

b. -

1-3 [*] 1-2

a. schizophrenia AND drug

b. for AND NOT (drug OR approach)

.

simple conjunctive query

Brutus AND Calpurnia -

-

() Brutus

()

() Calpurnia

()


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() -

- - Brutus Calpurnia

intersect ion

merge

merge algorithm

-

-

ID ID ID

ID x y

O(x + y) (N) N

(.) O(.)

Cormen et al.0

Brutus

Calpurnia

0

INTERSECT(p,p)

answer

whilep NIL. and p NIL

do ifdocI D( p) = doc I D (p)

then ADD(a'nswer, doc I D(p) )

pnext(p)

pnext(p)

else ifdoc. I D(p) < docI D(p)

thenpnext(p) elsepnext(p)

0 returnanswer


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ID

(Brutus OR Caesar) AND NOT Calpurnia -

query optimization

t

Brutus AND Caesar AND Calpurnia -

- -

(Calpurnia AND Brutus) AND Caesar -

(madding OR crowd) AND (ignoble OR strife) AND (killed OR slain) -

OR AND

AND


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-

-

bash

-

1-4 [*] O(x + y) x y Brutus

Caesar

a. Brutus AND NOT Caesarb. Brutus OR NOT Caesar

1-5 [*]

c. (Brutus OR Caesar) AND NOT (Antony OR Cleopatra

INTERSECT (t, ...,tn)

termsSORTBYINCREASINGFREQUENCY(t, ...,tn)

resultpostings(frst(terms))

termsrest(terms)

while termsNIL and result NIL

doresultINTERSECT(result,postings(frst(terms))) termsrest(terms)

returnresult


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1-6 [**] AND OR

a. -

b.

c.

1-7 [*]

d. (tangerine OR trees) AND (marmalade OR skies) AND (kaleidoscope OR eyes

eyes

kaleidoscope 00

marmalade 0

skies

tangerine

trees

1-8 [*]

e. friends AND romans AND (NOT countrymen

countrymen

1-9 [**]

1-10 [**] x OR y 1-6

1-11 [**] x AND NOT y

.

ranked retrieval

model .

free text query

P-norm


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0 0

0

ANDOR NOT

term proximityproximity

-

Westlaw Westlawhttp://www.

westlaw.com/ 0

00 Westlaw

Terms and Connectors

Westlaw Natural Language

Westlaw

Information on the legal theories involved in preventing the disclosure of trade

secrets by employees formerly employed by a competing company

trade secret/s disclos!/s prevent/s employe!

Requirements for disabled people

to be able to access a workplace

disab!/p access!/s work-site work-place (employment / place

Cases about a host's responsibility for

drunk guests

host!/p (responsib! liab!)/p (intoxicat! drunk!)/p guest


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Web

0 Web

& AND, /s

/p /k k

phrase search .

! . liab!

liab work-site worksitework-site

work site ..

Westlaw

00

Westlaw Turtle,

Westlaw

AND

OR

()

() operating system

Westlaw

Gates NEAR Microsoft

()


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()

ad hoc

ad hoc Web

Web

Web

1-12 [*] Westlaw professor

teacher lecturer explain

explain

1-13 [*] burglar

(i) burglar(ii) burglar AND burglar(iii) burglar OR burglar

(i) knight(ii) conquer(iii) knight OR conquer

.

0 0 Cleverdon; Liddy00


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Bush

memex

memex

Information Retrieval Calvin Mooers 0

Mooers0

IBM

Taube and Wooster, H. P. Luhn

Mooers

George

Boole

ANDOR

Lee and Fox,

Witten Witten et al.

Zobel and Moffat, 00

Friedl 00 regular expression

Hopcroft et al. 000


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531

A

A/B test A/B 177

Accents 32, 53

Access control lists 84

Accumulator 119, 132, 237

Accuracy 162, 285, 294, 295,

299, 373

Active learning 196, 350,

361

Add-one smoothing 275,

277, 278

Ad hoc retrieval ad hoc 6, 196,

264, 268, 282, 298, 315, 333, 334, 352,

353, 355, 358, 361

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

evaluation of 7, 46, 68, 145,

155, 157, 158, 159, 160, 161, 163, 165,

167, 169, 170, 171, 173, 174, 175, 176,

177, 180, 181, 182, 193, 194, 200, 203,

206, 212, 219, 221, 222, 225, 226, 233,

260, 267, 271, 294, 295, 297, 298, 301,

324, 347, 353, 360, 363, 365, 369, 370,

371, 372, 374, 386, 388, 415, 432, 445,

449, 471

machine learning methods

22, 42, 152, 155, 174, 226, 298,

333, 334, 347, 348, 350, 355, 361, 482

Adjacency tables 468, 486

Adjusted Rand index

388

Adversarial information retrieval

441

Akaike information criterion (AIC)

381

Algebra, linear, review

Algorithmic search

Anchor text 264, 411, 412,

454, 459, 474, 475, 476, 483, 489, 492

Any-of classification 322,

323, 332

Auxiliary index 81, 82, 85

Average-link clustering

403

B

Back queues 464, 465, 466

Bag of words model

Unigram language model 123, 124,

127, 254, 282, 283, 324, 355

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,


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359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,465, 479, 480, 481, 487, 492

Balanced F measure F F

measure F 163

Bayes error rate 316, 331

Bayesian networks 230, 246,

247

Bayesian prior 238, 240

Bayesian smoothing 265

Bayes Optimal Decision Rule 232

Bayes risk 233

Bayes Rule 231, 234, 280

Bernoulli model 259, 260,

267, 278, 279, 280, 281, 282, 283, 284,

285, 287, 289, 290, 292, 300, 383, 384

Best-merge persistence

400, 402, 404, 405, 413

Bias 254, 264, 286, 303, 305,323, 324, 325, 326, 327, 328, 331, 335,

349

Bias-variance tradeoff -

254, 286, 327, 335

Biclustering 389

Bigram language model

Binary Independence Model (BIM)

Binary search tree Biword indexes 42, 43,

44, 46

Blind relevance feedback

184, 194

Blocked sort-based indexing algorithm

BSBI 73,

74, 75, 76, 77, 78, 86

Blocked storage described

Blogs

BM25 weights BM25 230, 242,

244

Boolean retrieval 1, 4, 6, 11,15, 17, 19, 28, 38, 83, 113, 119, 123,

154, 204

model 4, 6, 9, 11, 15, 16, 17,

18, 19, 29, 33, 67, 68, 87, 92, 94, 105,

108, 111, 115, 116, 123, 124, 126, 127,

141, 142, 149, 151, 152, 153, 154, 178,

182, 188, 189, 191, 192, 200, 203, 204,

205, 206, 207, 215, 222, 223, 224, 226,

229, 230, 231, 232, 233, 234, 235, 237,238, 239, 240, 242, 243, 244, 246, 249,

250, 251, 252, 253, 254, 255, 256, 257,

139, 247, 258, 257, 258, 259, 260, 261,

262, 263, 264, 265, 267, 273, 274, 277,

278, 279, 280, 281, 282, 283, 284, 285,

286, 287, 289, 290, 292, 293, 300, 301,

303, 304, 305, 306, 323, 324, 326, 327,

328, 334, 335, 336, 338, 339, 343, 349,

355, 359, 363, 368, 380, 381, 382, 383,384, 386, 387, 389, 415, 432, 433, 440,

441, 442

principles 210, 229, 230, 232,

233, 265, 291, 406

query processing 9, 15,

17, 28, 30, 31, 38, 57, 82, 83, 99, 108,

113, 139, 147, 148, 151, 155, 466, 467,

470

ranked retrieval vs.

tokenization 8, 22, 25, 26,

27, 28, 34, 42, 95, 152, 230, 314

vector space model interactions

Boosting

Bottom-up cluster ing

. hierarchical agglomerative

clustering (HAC)


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393, 410

Bowtie structure 439

Break-even point

168BSBI (b locked sor t -based indexing

algorithm)

73, 74, 75, 76, 77, 78, 86

Buckshot algorithm Buckshot

378, 414

Buffer 71, 74, 181

C

Caching 71, 90, 109

compression and in search systems 90

variable length arrays and

Capitalization 32, 33

Capture-recapture method

Cardinality in clustering

Case-folding 33, 73, 91,

92, 93, 100

CAS topics CAS 220, 222, 224

Category 3, 22, 25, 161, 162,

172, 196, 238, 256, 268, 269, 270, 271,

272, 273, 274, 275, 277, 279, 280, 281,

282, 283, 284, 286, 287, 288, 289, 290,

291, 292, 293, 294, 295, 296, 297, 298,

299, 300, 304, 305, 307, 308, 309, 310,

311, 312, 313, 314, 316, 317, 318, 319,

320, 321, 322, 323, 324, 325, 326, 327,

328, 329, 330, 332, 334, 335, 336, 339,

340, 343, 344, 348, 349, 350, 351, 352,

357, 358, 360, 364, 371, 372, 379, 386,

388, 413, 435, 444, 445

Centroid-based classification

331

Centroids 188, 189, 190, 192,

304, 306, 307, 308, 309, 310, 311, 314,

327, 329, 331, 369, 374, 375, 376, 377,

378, 379, 381, 382, 384, 386, 388, 391,

392, 396, 400, 401, 404, 405, 406, 407,

409, 411, 412, 413, 414, 415, 432

HAC 392, 393 ,395, 396, 399, 400, 401, 403, 404, 405,

406, 408, 409, 410, 412, 413, 414

Rocchio classification Rocchio

307, 308, 309, 310, 311, 316, 329, 331,

374

Chaining in clustering

399

Chain rule 231

Champion lists 145, 146, 149,155

Character sequence decoding

21, 22, 23

2

feature selection 2 291,

292

Chinese 12, 29, 35, 48, 49, 67,

100, 124, 129, 142, 146, 147, 150, 174,

178, 207, 215, 244, 276, 305, 307, 319,

327, 334, 358, 368, 374, 407, 430, 453Class boundary 311, 319,

320

Classes 3, 22, 25, 161, 162, 172,

196, 238, 256, 268, 269, 270, 271, 272,

273, 274, 275, 277, 279, 280, 281, 282,

283, 284, 286, 287, 288, 289, 290, 291,

292, 293, 294, 295, 296, 297, 298, 299,

300, 304, 305, 307, 308, 309, 310, 311,

312, 313, 314, 316, 317, 318, 319, 320,321, 322, 323, 324, 325, 326, 327, 328,

329, 330, 332, 334, 335, 336, 339, 340,

343, 344, 348, 349, 350, 351, 352, 357,

358, 360, 364, 371, 372, 379, 386, 388,

413, 435, 444, 445

maximum a posteriori

C l a s s i f i c a t i o n T e x t

classification 3, 22, 25, 26, 27, 28,

126, 134, 158, 159, 160, 161, 162, 184,


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191, 197, 230, 260, 267, 268, 269, 270,

271, 272, 273, 275, 276, 277, 278, 279,

280, 282, 284, 285, 286, 287, 288, 289,

290, 292, 293, 294, 295, 296, 297, 298,299, 300, 303, 304, 305, 306, 307, 308,

309, 310, 311, 312, 313, 314, 315, 316,

317, 301, 318, 317, 318, 319, 320, 321,

322, 323, 324, 325, 326, 327, 328, 329,

330, 331, 332, 333, 334, 335, 336, 337,

339, 340, 341, 342, 343, 344, 345, 347,

348, 349, 350, 351, 352, 353, 354, 355,

356, 357, 358, 360, 361, 364, 370, 374,

380, 382, 384, 412, 427, 435, 436, 454,471, 483

any-of 272, 305, 321, 322,

323, 332

centroid-based 304, 331

k N N k n e a r e s t n e i g h b o r

classification (kNN) 284, 297, 298,

301, 304, 305, 306, 311, 312, 313, 314,

315, 316, 320, 321, 323, 326, 327, 329,

331, 343, 348, 349, 412multivalue 321

one-of 272, 299, 305, 321,

322, 323, 331

one-versus-all 343

R o c c h i o R o c c h i o

Rocchio classification 307, 308,

309, 310, 311, 316, 329, 331, 374

Classification function 271,

272, 304, 321, 339, 340Classifiers 22, 162, 191, 230,

268, 270, 271, 272, 275, 276, 278, 279,

284, 285, 286, 287, 289, 292, 293, 294,

295, 296, 297, 298, 299, 300, 301, 303,

305, 306, 310, 311, 312, 314, 315, 316,

317, 318, 319, 320, 321, 322, 323, 324,

325, 326, 327, 328, 329, 330, 331, 332,

334, 335, 336, 337, 339, 340, 342, 343,

344, 345, 347, 349, 350, 351, 352, 353,

354, 355, 356, 357, 358, 360, 361

choosing 18, 23, 25, 26, 30, 34,

36, 37, 40, 44, 52, 57, 60, 62, 64, 73,

74, 78, 81, 83, 85, 86, 106, 111, 117,122, 128, 130, 131, 144, 146, 148, 149,

154, 164, 168, 172, 173, 174, 177, 178,

181, 186, 196, 208, 210, 218, 224, 226,

245, 256, 262, 267, 271, 273, 274, 280,

282, 283, 284, 285, 286, 287, 288, 289,

290, 291, 292, 293, 298, 300, 301, 304,

312, 314, 318, 319, 323, 325, 327, 331,

335, 343, 344, 347, 349, 350, 351, 352,

354, 357, 361, 367, 370, 375, 376, 378,379, 380, 381, 382, 383, 386, 389, 392,

393, 395, 396, 403, 406, 409, 410, 411,

413, 414, 415, 435, 437, 438, 440, 441,

446, 447, 448, 449, 453, 457, 459, 464,

465, 466, 469, 470, 474, 475, 477, 478,

484, 487, 489, 492

performance improving

3, 9, 10, 11, 13, 18, 37, 39, 40, 46,

48, 49, 58, 62, 77, 90, 108, 109, 112,131, 135, 139, 149, 165, 173, 179, 182,

185, 186, 189, 190, 192, 193, 194, 195,

196, 198, 199, 219, 223, 238, 265, 271,

285, 286, 288, 289, 315, 322, 334, 335,

343, 347, 350, 351, 352, 353, 354, 360,

366, 367, 368, 388, 412, 430, 431, 436,

440, 444, 448, 449, 450, 457, 474, 476,

493

two-class 3, 101, 135, 139,172, 178, 184, 268, 281, 294, 296, 307,

309, 310, 317, 319, 320, 326, 327, 331,

335, 337, 339, 343, 346, 373, 386, 435,

456, 487

CLEF collection CLEF

Click spam 443

Clickstream mining 177,

195

Clickthrough log analysis


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177, 181

Cliques 246, 398

Cloaking, in spamming

Cluster-based classification 331

Cluster hypothesis 365, 367,

368, 387

Clustering 2, 3, 26, 126, 147,

148, 265, 309, 352, 363, 364, 365, 366,

367, 368, 369, 370, 371, 372, 373, 374,

376, 377, 378, 379, 380, 381, 382, 383,

384, 385, 386, 387, 388, 389, 391, 392,

393, 394, 395, 396, 397, 398, 399, 400,401, 402, 403, 404, 405, 406, 407, 408,

409, 410, 411, 412, 413, 414, 415, 418,

427, 431, 432, 453, 467

average-link 403

cardinality in 30, 110, 181, 192,

223, 226, 232, 242, 285, 286, 301, 314,

334, 379, 434

centroid-based 304, 331

chaining in 399, 409complete-link HAC HAC

divisive 391, 392, 409, 410,

415

exclusive vs. exhaustive

flat Flat clustering

363, 364, 365, 368, 370, 374, 379, 392,

394, 395, 409, 410, 413, 414

group-average agglomerative 391, 400, 403

hard 19, 40, 69, 70, 71, 75, 77,

78, 87, 90, 96, 112, 163, 179, 260, 331,

347, 365, 368, 369, 381, 383, 384, 386,

437, 466, 490

hierarchical Hierarchical

clustering 150, 151, 152, 153, 210,

221, 272, 351, 360, 391, 392, 394, 395,

407, 409, 410, 415, 434, 435, 446

minimum variance 414

model-based 264, 363,

382, 383, 415

optimal 13, 15, 40, 103, 104,105, 110, 112, 188, 232, 233, 245, 259,

277, 293, 298, 301, 314, 316, 323, 324,

325, 331, 334, 339, 340, 342, 370, 376,

378, 379, 380, 386, 389, 391, 392, 407,

408, 409, 414, 469

overview 19, 455, 456

single-link HAC HAC

spectral 415

top-down 211, 392, 393,409, 410

Clusters 70, 77, 80, 466

pruning 155

Co-clustering 389

Collections 2, 4, 6, 9, 11, 12,

15, 17, 18, 25, 30, 34, 44, 45, 47, 52,

58, 60, 65, 70, 72, 73, 74, 75, 77, 78,

79, 80, 81, 85, 90, 91, 92, 93, 94, 95,

96, 97, 98, 99, 100, 101, 106, 108, 109,111, 112, 116, 117, 119, 121, 124, 125,

128, 129, 130, 131, 135, 136, 137, 138,

144, 148, 151, 152, 158, 159, 160, 161,

162, 165, 167, 168, 169, 170, 171, 173,

174, 175, 176, 178, 181, 184, 185, 188,

189, 191, 192, 194, 196, 198, 199, 200,

204, 207, 208, 210, 212, 213, 214, 216,

217, 219, 220, 224, 225, 232, 234, 235,

237, 238, 239, 240, 241, 242, 244, 246,255, 256, 258, 259, 260, 261, 262, 264,

268, 271, 277, 288, 292, 293, 296, 298,

300, 304, 307, 311, 312, 314, 315, 328,

358, 359, 365, 366, 367, 368, 370, 371,

373, 377, 383, 388, 392, 395, 397, 400,

409, 412, 413, 415, 418, 424, 427, 428,

431, 434, 457, 467

clustering 2, 3, 26, 126, 147,

148, 265, 309, 352, 363, 364, 365, 366,


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367, 368, 369, 370, 371, 372, 373, 374,

376, 377, 378, 379, 380, 381, 382, 383,

384, 385, 386, 387, 388, 389, 391, 392,

393, 394, 395, 396, 397, 398, 399, 400,401, 402, 403, 404, 405, 406, 407, 408,

409, 410, 411, 412, 413, 414, 415, 418,

427, 431, 432, 453, 467

frequency 9, 10, 13, 14, 15,

18, 30, 43, 45, 46, 47, 53, 65, 71, 72,

79, 83, 86, 91, 92, 94, 95, 96, 97, 100,

104, 106, 108, 113, 115, 123, 124, 125,

129, 131, 132, 133, 134, 135, 138, 139,

149, 155, 158, 176, 177, 178, 190, 192,213, 217, 218, 219, 234, 238, 239, 241,

244, 245, 246, 257, 258, 261, 262, 263,

274, 275, 279, 285, 286, 290, 292, 293,

294, 304, 315, 359, 372, 412, 415, 424,

439, 448, 456, 457, 479, 481

residual defined

statistics 7, 9, 83, 85, 91,

116, 191, 198, 213, 218, 219, 220, 256,

355, 467large 96, 111

Combination schemes

46

Combination similarity

393, 394, 398, 407, 408, 409

Complete-linkage clustering

391, 396, 397, 398, 399, 402, 403,

404, 408

Complete-link clustering 406

Component coverage 220,

221

Compound nouns 28, 360

Compound-splitter 28

Compression 7, 14, 23, 30, 40,

44, 45, 70, 71, 73, 75, 77, 83, 86, 89,

90, 91, 92, 94, 95, 96, 97, 98, 99, 100,

101, 102, 97, 98, 99, 100, 101, 102,

103, 104, 105, 106, 107, 108, 109, 110,

111, 112, 109, 112, 113, 99, 169, 468,

104, 105, 106, 107, 108, 112, 109, 110,

111, 112, 113, 103of dictionaries 89, 91, 94,

95, 98, 100, 109, 112

of docIDs ID

lossless/lossy

parameter-free

parameterized

of postings list 91

Compression/indexes

Heaps law Heaps 93, 111, 315overview 19, 455, 456

Zipfs law Zipf 94, 95, 106,

108, 111, 439

Concept drift 284, 285, 298,

301

Conditional independence assumption

235, 281, 282, 283

Confusion matrix 322, 323,

386Connected components 398

Connectivity queries 467,

468, 470

Connectivity servers 455,

467, 471

Content management systems

70, 87

Content seen module

461Context, XML XML 216, 217,

218

Context resemblance

216, 219

Contiguity hypothesis 304,

311, 365

Continuation bit 101, 102

Corpus 6, 30, 72, 73, 74, 75,

102, 161, 294


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Cosine similarity 127,

128, 129, 130, 138, 139, 142, 143, 144,

145, 147, 148, 150, 188, 217, 243, 306,

309, 313, 329, 355, 356, 369, 387, 393,404, 427, 428, 476

CO topics CO 220, 224

CPC (cost per click)

442

CPM (cost per mil)

441

Cranfield collection Cranfield

Cross-entropy 264

Cross-language information retrieval 161, 432, 490

Cumulative gain 169, 181

D

Databases 2, 70, 87, 204, 205,

206, 223, 224, 226, 227, 437

communication with

re la t ional 204, 223,

224, 226, 227

-codes 103, 110, 112

Decision boundaries 307,

317, 319, 320, 326, 327, 328, 329, 334,

336, 337, 339, 341, 347, 357, 359

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

Decision hyperplanes 305,

317, 319, 320, 335, 336Decision trees 297, 300, 334,

350

Dendrograms 393, 394, 397,

398, 406, 413

complete-link clustering

391, 396, 397, 398, 399, 402, 403,

404, 408

described 61, 142, 144, 160,

168, 198, 204, 205, 207, 209, 222, 224,231, 242, 260, 275, 285, 301, 310, 320,

322, 324, 349, 363, 367, 368, 378, 381,

396, 404, 412, 432, 454, 468, 471, 474,

475, 476, 491

Development sets 298, 349

Development test collection

159, 245

Diacritics 32, 33

Dice coefficient Dice 170Dictionaries 7, 8, 9, 10, 11, 13,

17, 21, 22, 25, 26, 28, 29, 31, 34, 35,

38, 51, 52, 53, 54, 55, 57, 58, 59, 60,

75, 76, 77, 80, 86, 89, 90, 91, 92, 94,

95, 96, 97, 98, 48, 67, 97, 98, 99, 100,

109, 112, 117, 118, 125, 143, 145, 154,

184, 196, 197, 198, 199, 200, 201, 254,

260, 264, 283, 448, 466, 468, 469

compression of 89, 91,94, 95, 98, 100, 109, 112

in inverted indexes

52

search structures for

Differential cluster labeling

410

Digital libraries 204

Discrete-time stochastic processes

478


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Disk seek 11, 81

Distortion 380, 381, 395

Distributed crawling 461,

466, 471Distributed index 69, 70,

77, 78, 80, 86, 455, 456, 466, 471

Distributed indexing

Distributed information retrieval

Divisive clustering 391,

409, 410, 415

DNS resolution DNS 462, 463,

471DNS resolution module DNS

DNS server DNS 462, 463

DocIDs ID 9, 10, 41, 45, 79, 82,

83, 92, 100, 101, 102, 109, 112, 113,

121, 146, 147, 150, 151, 261, 276, 299,

356, 385

compression of ID

in inverted indexes

IDin postings list intersection operations

Document-at-a-time scoring

Document co l lec t ion

Collections 2, 4, 6, 9, 11, 12, 15, 17,

18, 25, 30, 34, 44, 45, 47, 52, 58, 60,

65, 70, 72, 73, 74, 75, 77, 78, 79, 80,

81, 85, 90, 91, 92, 93, 94, 95, 96, 97,98, 99, 100, 101, 106, 108, 109, 111,

112, 116, 117, 119, 121, 124, 125, 128,

129, 130, 131, 135, 136, 137, 138, 144,

148, 151, 152, 158, 159, 160, 161, 162,

165, 167, 168, 169, 170, 171, 173, 174,

175, 176, 178, 181, 184, 185, 188, 189,

191, 192, 194, 196, 198, 199, 200, 204,

207, 208, 210, 212, 213, 214, 216, 217,

219, 220, 224, 225, 232, 234, 235, 237,

238, 239, 240, 241, 242, 244, 246, 255,

256, 258, 259, 260, 261, 262, 264, 268,

271, 277, 288, 292, 293, 296, 298, 300,

304, 307, 311, 312, 314, 315, 328, 358,359, 365, 366, 367, 368, 370, 371, 373,

377, 383, 388, 392, 395, 397, 400, 409,

412, 413, 415, 418, 424, 427, 428, 431,

434, 457, 467

Document likelihood model

263

Document-partitioned index

77

Documents 15, 37, 60, 151,178, 204, 207, 216, 223, 227, 268, 270,

275, 431, 434, 476

character sequence decoding

classification of

Text classification 126, 330, 350,

353

defined

delineation of 21, 22, 35, 37, 38,42, 47, 75, 78, 79, 80, 86, 92, 125, 139,

151, 152, 153, 163, 171, 173, 177, 181,

194, 197, 198, 199, 201, 205, 239, 246,

301, 322, 330, 346, 388, 389, 410, 420,

421, 440, 441, 454, 457, 458, 459, 462,

466, 467, 473, 474, 475, 476, 489, 490,

492, 493

frequency defined

function notations partitioning 38, 49, 73, 77, 78,

79, 80, 211, 256, 369, 370, 371, 409,

410, 415, 463

relevant, retrieving

unit, choosing

vector, defined 327

Document space 234, 271,

280, 304, 352

Document zones 123, 353,


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355

Doorway pages 440

Dot products 127, 128, 130, 131,

136, 306, 308, 329, 339, 342, 343, 345,346, 402, 403, 404, 412

described 61, 142, 144, 160,

168, 198, 204, 205, 207, 209, 222, 224,

231, 242, 260, 275, 285, 301, 310, 320,

322, 324, 349, 363, 367, 368, 378, 381,

396, 404, 412, 432, 454, 468, 471, 474,

475, 476, 491

in SVMs SVM

Duplicate elimination modules 459

Dynamic indexing 69, 70,

80, 83, 105, 467

Dynamic summary 178, 179,

180, 181

E

East Asian languages

ChineseJapanese 28, 48, 161

Edit distance 59, 61, 62, 63,

64, 65, 67

Effectiveness 7, 28, 33, 37, 38,

40, 42, 46, 48, 49, 68, 92, 110, 111,

112, 127, 135, 158, 160, 161, 162, 163,

166, 168, 173, 177, 180, 181, 182, 185,

189, 190, 191, 192, 193, 194, 199, 200,

219, 222, 224, 232, 247, 257, 258, 260,

261, 262, 263, 272, 279, 283, 284, 289,

290, 292, 293, 295, 296, 297, 298, 301,

313, 315, 317, 320, 322, 323, 328, 331,

334, 347, 349, 350, 351, 352, 353, 354,

360, 361, 364, 365, 367, 368, 370, 381,

388, 399, 420, 431, 458, 490

assessment of 27, 34, 157,

158, 159, 160, 168, 170, 171, 172, 173,

174, 181, 189, 190, 191, 193, 194, 195,

196, 212, 220, 230, 269, 288, 289, 292,

296, 304, 312, 317, 351, 355, 357, 358,

359, 370, 371

text classification 158,

160, 161, 260, 267, 268, 269, 270, 271,

272, 273, 276, 277, 280, 284, 285, 286,292, 293, 294, 295, 297, 298, 300, 303,

301, 304, 301, 303, 304, 305, 309, 310,

311, 315, 316, 319, 323, 324, 325, 328,

331, 333, 334, 341, 343, 347, 348, 350,

351, 352, 353, 354, 355, 360, 361, 382

Efficiency 9, 10, 13, 15, 31, 39,

46, 49, 62, 72, 77, 82, 103, 105, 109,

110, 112, 131, 135, 149, 190, 192, 195,

204, 271, 286, 293, 295, 296, 305, 315,329, 350, 352, 365, 368, 378, 388, 392,

402, 410, 412, 457

Eigen decomposition 421,

422

Eigenvalues 418, 419, 420,

421, 422, 423, 425, 427, 478, 480, 488

11-point interpolated average precision

11 166

Email 27document units 23, 25,

210

sorting 3, 4, 9, 13, 19, 25, 33,

72, 79, 117, 160, 213, 269, 350, 360,

399, 446, 466

EM algorithm EM 259, 365,

382, 383, 384, 385, 386, 387, 389, 392

Enterprise resource planning

87, 205Enterprise search 70, 87, 95

Entropy 104, 105, 106, 112, 264,

300, 372, 373

Equivalence classes 22, 31, 32,

34, 37

Ergodic Markov Chain

479, 480, 484

Euclidean distance

138


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Euclidean length 127

Evalution of retrieval systems

7, 160

A/B test A/B 177ad hoc ad hoc 6, 196, 264,

268, 282, 298, 315, 333, 334, 352, 353,

355, 358, 361

clustering 2, 3, 26, 126, 147,

148, 265, 309, 352, 363, 364, 365, 366,

367, 368, 369, 370, 371, 372, 373, 374,

376, 377, 378, 379, 380, 381, 382, 383,

384, 385, 386, 387, 388, 389, 391, 392,

393, 394, 395, 396, 397, 398, 399, 400,401, 402, 403, 404, 405, 406, 407, 408,

409, 410, 411, 412, 413, 414, 415, 418,

427, 431, 432, 453, 467

F measure F 163, 164, 165, 168,

180, 221, 371, 374, 388

interpolated precision

165, 166, 167, 170, 171

kappa statistic kappa 172,

173, 175, 181keyword-in-context snippets

MAP 167

marginal relevance 222

normalized discounted cumulative gain

169

overview 19, 455, 456

pooling 168, 171, 181, 296

precision at k kprecision-recall curve -

165, 166, 167, 168, 169, 193

probabilistic information retrieval

229, 234, 242, 243, 247, 229,

234, 242, 243, 247, 265

ranked sets

relevance assessment

157, 158, 159, 160, 168, 170, 171, 172,

173, 174, 181, 194, 212, 220, 355, 357,

359

relevance feedback

177, 183, 184, 185, 186, 187,

188, 189, 190, 191, 192, 193, 194, 195,200, 193, 196, 193, 200, 184, 185, 186,

187, 188, 196, 188, 189, 190, 191, 192,

193, 194, 193, 194, 195, 196, 200, 230,

234, 236, 239, 240, 241, 242, 244, 246,

230, 234, 236, 195, 240, 241, 242, 244,

246, 230, 234, 236, 239, 240, 241, 242,

244, 246, 230, 234, 236, 239, 240, 241,

242, 244, 246, 262, 263, 264, 262, 263,

264, 262, 263, 264, 262, 263, 239, 309,310, 311, 330, 309, 310, 311, 330, 309,

310, 311, 330, 309, 310, 311, 330, 194,

195, 196, 200, 194, 195, 264

results snippets 157, 177,

178, 179, 180, 181

ROC curve ROC 169, 361

R-precis ion R 168, 169,

170, 180, 181

sensitivity 169specificity

summarization, static vs. dynamic

system quality/user utility

test collections, standard

157, 158, 160

text classification 158,

160, 161, 260, 267, 268, 269, 270, 271,272, 273, 276, 277, 280, 284, 285, 286,

292, 293, 294, 295, 297, 298, 300, 303,

301, 304, 301, 303, 304, 305, 309, 310,

311, 315, 316, 319, 323, 324, 325, 328,

331, 333, 334, 341, 343, 347, 348, 350,

351, 352, 353, 354, 355, 360, 361, 382

text summarization 152,

158, 180, 195, 415

unranked sets 224


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XML retrieval XML 203, 205,

206, 209, 210, 212, 213, 215, 219, 221,

222, 223, 224, 219, 226, 205, 206, 209,

222, 212, 213, 215, 219, 221, 222, 223,224, 226, 227, 262, 221, 222, 223, 224,

226, 227, 224, 226, 227

Evidence accumulation

Exclusive clustering

Exhaustive clustering 369

E x p e c t a t i o n - M ax i m i z a t i o n ( E M )

algori thm EM 259, 365,

382, 383, 384, 385, 386, 387, 389, 392

Expectation step E 383, 384, 387Expected edge density

388

Extended query 196, 214,

216

Extensible Markup Language

XML 205

External criterion of quality

External sorting algorithm 73, 75

F

False negative 162, 371, 386

False positive 162, 163, 169,

371, 386

Feature engineering 331,

352, 354, 359

Feature selection/text classification

2 2 286, 290

frequency-based 292, 293

method comparison

multiple classifiers 343

mutual information 286,

287, 288, 289, 292, 294, 300, 301, 352,

354, 371, 372, 386, 411

noise feature 284, 286,

287, 316, 319

overfitting 286, 356

overview 19, 455, 456

in performance improvement

350, 351statistical significance

180, 260, 292, 293, 301

Fetch modules

Field 116, 117, 118, 152, 204,

206, 223, 224, 247

Filtering 2, 6, 56, 57, 58, 81, 91,

196, 209, 223, 268, 273, 327, 330, 348,

350, 352, 450, 459, 460, 461

First story detection 409,414

Flat clustering 363, 364,

365, 368, 370, 374, 379, 392, 394, 395,

409, 410, 413, 414

Akaike information criterion AIC

381, 387

cardinality in 30, 110, 181, 192,

223, 226, 232, 242, 285, 286, 301, 314,

334, 379, 434classification vs.

collections 2, 4, 6, 9, 11, 12,

15, 17, 18, 25, 30, 34, 44, 45, 47, 52,

58, 60, 65, 70, 72, 73, 74, 75, 77, 78,

79, 80, 81, 85, 90, 91, 92, 93, 94, 95,

96, 97, 98, 99, 100, 101, 106, 108, 109,

111, 112, 116, 117, 119, 121, 124, 125,

128, 129, 130, 131, 135, 136, 137, 138,

144, 148, 151, 152, 158, 159, 160, 161,162, 165, 167, 168, 169, 170, 171, 173,

174, 175, 176, 178, 181, 184, 185, 188,

189, 191, 192, 194, 196, 198, 199, 200,

204, 207, 208, 210, 212, 213, 214, 216,

217, 219, 220, 224, 225, 232, 234, 235,

237, 238, 239, 240, 241, 242, 244, 246,

255, 256, 258, 259, 260, 261, 262, 264,

268, 271, 277, 288, 292, 293, 296, 298,

300, 304, 307, 311, 312, 314, 315, 328,


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358, 359, 365, 366, 367, 368, 370, 371,

373, 377, 383, 388, 392, 395, 397, 400,

409, 412, 413, 415, 418, 424, 427, 428,

431, 434, 457, 467defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

distortion 380, 381, 395

evaluation of 7, 46, 68, 145,155, 157, 158, 159, 160, 161, 163, 165,

167, 169, 170, 171, 173, 174, 175, 176,

177, 180, 181, 182, 193, 194, 200, 203,

206, 212, 219, 221, 222, 225, 226, 233,

260, 267, 271, 294, 295, 297, 298, 301,

324, 347, 353, 360, 363, 365, 369, 370,

371, 372, 374, 386, 388, 415, 432, 445,

449, 471

exhaustive 369, 409Expectation-Maximization algorithm

EM 259, 365, 382, 383, 384,

385, 386, 387, 389, 392

expectation step E 383, 384, 387

external criterion of quality

HAC vs . HAC

internal criterion of quality

K means K

K-medoids K- 415

in language models

maximization step M 383, 384,

387

model complexity 380,

381

normalized mutual information

371

objective functions 341,

369, 370, 375, 377, 379, 380, 382

outliers 334, 341, 377, 378,396, 399

partitional 369

purity 371, 372, 373, 374, 386

Rand index, adjusted

388

residual sum of squares

374, 380, 395

scatter-gather - 366, 388

search result 7, 17, 32, 82,84, 222

seeds 375, 376, 377, 378, 384,

386, 389, 413, 457

singleton 378

soft 3, 23, 33, 36, 87, 96, 139,

150, 329, 340, 341, 342, 347, 365, 369,

383, 384, 385, 386, 387, 389, 392, 415,

424, 431, 432, 446

unsupervised learning F measure F 163, 164, 165, 168,

180, 221, 371, 374, 388

Focused retrieval 222

Free text

Free text query

p a r s i n g f u n c t i o n s

designing 49, 53, 70, 71, 77,

80, 87, 96, 100, 150, 151, 152, 158,

171, 176, 178, 179, 209, 212, 213, 224,


51/87

543

244, 254, 307, 329, 435, 446, 456, 457,

458, 463, 466, 475

tokenization 8, 22, 25, 26,

27, 28, 34, 42, 95, 152, 230, 314in vector retrieval models

234

Frequency-based feature selection

292

Frobenius norm F 425, 426, 430

Front coding 98, 99, 100,

109

Front queues 464, 465, 466,

474Functional margins 336

G

GAAC Group-average agglomerative

clustering 403, 404, 405, 406, 407,

408, 409, 412, 413, 414, 415

encoding 103, 104, 105,

106, 107, 108, 109, 110, 111

Gaps, encoding 468, 470

Generative model 278,

327

Geometric margin 337

Global champion list

Gold standard 159, 370, 371

Golomb codes Golomb 112

GOV2 collection GOV2

Greedy feature selection

Grepping

Ground truth 159

Group-average agglomerative clustering

391, 400, 403

Group-average clustering

403, 405, 413

H

HAC hierarchical agglomerative

clustering (HAC) 392, 393, 395,

396, 399, 400, 401, 403, 404, 405, 406,

408, 409, 410, 412, 413, 414

Hard assignment 365, 381, 384

Hard clustering 365, 369, 383,386

Harmonic numbers 106

Hashing 14, 52, 53, 55, 66, 76,

99, 331, 450, 451, 461, 467, 470

Heaps law Heaps 93, 111, 315

Held-out data 298, 313

Hierarchical agglomerative clustering

(HAC) 392, 393,

395, 396, 399, 400, 401, 403, 404, 405,406, 408, 409, 410, 412, 413, 414

algorithm comparison

best-merge persistence

400, 402, 404, 405, 413

Buckshot algorithm Buckshot

378, 414

centroids 188, 189, 190, 192,

304, 306, 307, 308, 309, 310, 311, 314,

327, 329, 331, 369, 374, 375, 376, 377,378, 379, 381, 382, 384, 386, 388, 391,

392, 396, 400, 401, 404, 405, 406, 407,

409, 411, 412, 413, 414, 415, 432

chaining in 399, 409

cliques 246, 398

cluster-internal labeling

combination similarity

complete-link clustering 391, 396, 397, 398, 399, 402, 403,

404, 408

connected components

398

dendrograms 393, 394, 397,

398, 406, 413

differential cluster labeling

410

divisive 391, 392, 409, 410,


52/87

544

415

first story detection

409, 414

flat vs. group-average 391, 392,

396, 400, 401, 403, 404, 405, 409, 413,

414, 415

inversions 393, 405, 406, 407,

409

monotonicity 393, 406, 413

next-best merge (NBM) arrays

novelty detection 388,409

optimality 13, 301, 314,

391, 392, 407, 408

outliers 334, 341, 377, 378,

396, 399

overview 19, 455, 456

priority queue algorithm

401

single-link clustering 394, 396, 397, 398, 399, 400, 402, 408,

409, 413, 414, 453

time complexity 12,

13, 14, 17, 39, 61, 75, 77, 81, 276, 277,

278, 299, 300, 311, 314, 315, 329, 331,

342, 343, 378, 379, 386, 392, 399, 400,

403, 404, 405, 409, 410, 412, 413, 414

top-down 211, 392, 393,

409, 410Hierarchical classification

351, 360

Hierarchical clustering

agg lomera t ive hiera rch ica l

agglomerative clustering (HAC)

applications 6, 10, 18, 23, 37,

44, 70, 72, 74, 77, 83, 86, 87, 96, 102,

108, 118, 123, 137, 139, 143, 144, 145,

151, 152, 158, 160, 168, 169, 173, 174,

176, 181, 189, 191, 192, 193, 194, 198,

204, 205, 208, 213, 215, 221, 224, 226,

243, 252, 253, 254, 257, 258, 264, 268,269, 271, 272, 275, 280, 286, 297, 298,

299, 305, 310, 315, 327, 331, 334, 344,

346, 347, 348, 349, 350, 351, 352, 360,

361, 363, 365, 366, 367, 368, 370, 375,

381, 382, 383, 384, 386, 387, 388, 389,

392, 394, 395, 407, 408, 409, 410, 413,

414, 418, 425, 432, 434, 436, 437, 448,

450, 451, 453, 454, 457, 465, 467, 468

defined 2, 3, 4, 17, 22, 25, 26,29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

probabilistic interpretation of

R environment support for R

Hierarchical Dirichlet processes HDP

Hierarchy 272, 351, 392,

394, 395, 409, 410, 415, 434, 435

Highlighting

HITS (hyperlink-induced topic search)

474, 489, 490,

491, 493


53/87

545

Host splitters 461

HTML 24, 178, 205,

224, 434, 435, 438, 440, 446, 453, 460,

474, 475http 16, 27, 36, 70,

86, 87, 161, 180, 181, 185, 186, 201,

340, 366, 367, 370, 388, 415, 432, 434,

435, 440, 446, 448, 459, 460, 475

Hub score 487

Hyperlink-induced topic search (HITS)

Hyperlinks Link analysis

Hyphenation and tokenization 3, 438, 439, 474, 487

I

Ide dec-hi 190, 200

IDF Inverse document frequency

(IDF) 190

IID Independent and identically

distributed (IID)

Images, searching for

Relevance feedback 177,

183, 184, 185, 186, 187, 188, 189, 190,

191, 192, 193, 194, 195, 200, 193, 196,

193, 200, 184, 185, 186, 187, 188, 196,

188, 189, 190, 191, 192, 193, 194, 193,

194, 195, 196, 200, 230, 234, 236, 239,

240, 241, 242, 244, 246, 230, 234, 236,

195, 240, 241, 242, 244, 246, 230, 234,

236, 239, 240, 241, 242, 244, 246, 230,

234, 236, 239, 240, 241, 242, 244, 246,

262, 263, 264, 262, 263, 264, 262, 263,

264, 262, 263, 239, 309, 310, 311, 330,

309, 310, 311, 330, 309, 310, 311, 330,

309, 310, 311, 330, 194, 195, 196, 200,

194, 195, 264

Impact ordering 84, 147

Implicit relevance feedback

177, 193, 195

Incidence matrix 5, 108, 109

Independence 233, 234, 235,

242, 243, 247, 274, 281, 282, 283, 285,

290, 291, 292, 293, 299, 301, 321

Independent and identically distributed(IID)

Index construction

BSBI 73, 74, 75, 76, 77, 78, 86

distributed indexes 69,

70, 77, 78, 80, 86, 455, 456, 466, 471

resources 3, 81, 87, 96, 180,

195, 197, 205, 264, 434, 457

Indexer 70, 78, 111, 152,

153, 180, 440, 445, 457, 459Indexes 1, 4, 5, 7, 8, 9, 10, 11,

13, 17, 18, 19, 22, 23, 24, 25, 26, 27,

28, 29, 30, 31, 34, 36, 38, 39, 40, 42,

43, 44, 45, 46, 47, 48, 49, 52, 55, 56,

57, 58, 59, 62, 63, 64, 65, 66, 67, 69,

70, 72, 73, 74, 75, 76, 77, 78, 79, 80,

81, 82, 83, 84, 85, 86, 87, 89, 90, 91,

92, 95, 99, 102, 105, 106, 107, 108,

109, 110, 111, 112, 113, 115, 116, 117,118, 119, 124, 128, 129, 130, 131, 138,

142, 144, 145, 146, 147, 150, 151, 152,

153, 154, 155, 158, 161, 175, 178, 179,

180, 184, 196, 197, 199, 204, 205, 210,

211, 212, 216, 218, 224, 225, 230, 268,

269, 315, 331, 365, 367, 368, 377, 412,

417, 418, 427, 431, 433, 434, 435, 436,

437, 440, 445, 446, 447, 448, 449, 450,

451, 453, 454, 455, 456, 457, 459, 466,467, 468, 470, 471, 475, 490

biword 42, 43, 44, 46, 155

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,


54/87

546

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

document-partitioned

k-gram k-gram 48, 57, 65

next word 46, 98parametric 40, 47, 49, 61, 70,

71, 75, 80, 85, 93, 105, 112, 115, 116,

117, 144, 145, 148, 153, 159, 160, 173,

174, 177, 189, 206, 242, 244, 245, 247,

254, 257, 258, 259, 263, 273, 274, 276,

278, 280, 281, 282, 283, 284, 287, 289,

298, 299, 300, 301, 305, 309, 311, 314,

316, 317, 318, 323, 327, 330, 331, 337,

341, 347, 353, 354, 357, 359, 361, 379,381, 382, 383, 384, 385, 386, 387, 477

permuterm 56, 57, 58, 59, 62,

67

positional 7, 8, 10, 12, 21, 24,

29, 39, 40, 41, 43, 44, 45, 46, 47, 48,

71, 72, 80, 83, 90, 91, 92, 95, 98, 100,

113, 116, 152, 153, 165, 167, 170, 178,

179, 215, 230, 254, 274, 278, 280, 281,

282, 283, 285, 299, 318, 328, 334, 335,350, 354, 366, 376, 434, 443, 444, 461,

469, 477, 479

size/estimation

term-partitioned

zone 71, 116, 117, 118, 119,

120, 123, 148, 178, 206, 304, 305, 306,

307, 308, 310, 312, 319, 320, 322, 329,

353, 354, 355, 357, 396, 402

Indexing

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

distributed 3, 69, 70, 77,

78, 79, 80, 86, 455, 456, 457, 459, 461,

462, 466, 467, 471

granularity 24, 25, 34, 103,

124, 173, 174, 222, 255, 388, 437

latent semantic 92, 199,368

unit defined

INEX 174, 206, 219, 220, 222, 223,

225, 227

Informational queries 444,

445

Information gain 300, 301,

410, 415

Information need 2, 3, 6, 7,16, 17, 135, 158, 159, 160, 166, 167,

168, 169, 170, 171, 174, 175, 178, 185,

193, 196, 210, 220, 230, 234, 245, 246,

257, 262, 268, 365, 368

Information retrieval 1, 2,

3, 4, 5, 7, 25, 26, 70, 83, 89, 90, 91, 94,

95, 103, 120, 6, 126, 18, 30, 84, 113,

135, 149, 141, 157, 158, 160, 161, 162,

171, 173, 184, 185, 189, 191, 196, 209,


55/87

547

220, 247, 249, 250, 253, 268, 363, 365,

441

hardware issues 78, 87

history of 253, 254, 294, 300,393, 407, 433, 434, 439, 443, 445, 465,

484

overview 19, 455, 456

search system components

terms, statistical properties of

89, 91

In-links 438, 474, 477, 482

Inner product Dot products 127, 128, 130, 131, 136, 306,

308, 329, 339, 342, 343, 345, 346, 402,

403, 404, 412

Instance-based learning

314

Internal criterion of quality

Interpolated precision

165, 166, 167, 170, 171Intersection, postings list

Inter-similarity

Inverse document frequency (IDF)

217

Inversions 393, 405, 406, 407,

409

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,465, 479, 480, 481, 487, 492

in HAC HAC 393, 395, 396,

399, 400, 403, 404, 405, 408, 409, 410,

412, 413, 414

Inverted file Inverted

indexPostings list 7, 100, 146

Inverted index 1, 4, 7, 8, 9,

10, 11, 17, 19, 22, 40, 48, 52, 55, 56,

57, 58, 66, 67, 70, 72, 73, 74, 75, 76,77, 79, 82, 84, 87, 90, 92, 105, 106,

107, 108, 109, 111, 113, 117, 119, 130,

142, 144, 147, 152, 153, 196, 204, 205,

218, 315, 331, 367, 368, 412, 435, 468

Boolean query processing

building principles

described 61, 142, 144, 160,

168, 198, 204, 205, 207, 209, 222, 224,231, 242, 260, 275, 285, 301, 310, 320,

322, 324, 349, 363, 367, 368, 378, 381,

396, 404, 412, 432, 454, 468, 471, 474,

475, 476, 491

encoding 103, 104, 105,

106, 107, 108, 109, 110, 111

kNN classification in kNN

Inverter 79, 80, 85

IP address IP 27, 462

J

Jaccard coefficient Jaccard 64 ,

65, 451, 452, 453

Japanese 28, 34, 35, 48, 55, 491

Journal influence weight

492

K

Kappa s ta t is t ic kappa 172,


56/87

548

173, 175, 181

Kernel function 346

Kernels 7, 9, 36, 49, 61, 62, 73,

196, 197, 204, 209, 268, 340, 345, 346,347, 348, 359, 360, 419, 487

Mercer Mercer 346

polynomial 254, 256, 257,

258, 259, 260, 273, 274, 275, 276, 277,

278, 279, 280, 281, 282, 283, 284, 285,

286, 289, 290, 292, 293, 299, 300, 321,

329, 346, 347, 419

quadratic 82, 105, 323, 346

radial basis functions 346, 347

Kernel trick 345

Keys 5, 18, 30, 151, 154,

171, 178, 179, 192, 209, 213, 220, 222,

270, 352, 353, 354, 366, 367, 435, 439,

440, 442, 443, 444, 449

Key-value pairs - 78, 79, 80, 85

Keyword-in-context (KWIC) snippets

k-gram index k-gram 48, 57, 65

described 61, 142, 144, 160,

168, 198, 204, 205, 207, 209, 222, 224,

231, 242, 260, 275, 285, 301, 310, 320,

322, 324, 349, 363, 367, 368, 378, 381,

396, 404, 412, 432, 454, 468, 471, 474,

475, 476, 491

spelling correction in 51,

52, 59, 60, 62, 63, 64, 65, 67, 68, 83,85, 152, 153, 184, 191

word matching in 17, 59

K means K

K-medoids K- 415

k nearest neighbor classification (kNN)

algorithm k 311

Bayes error rate 316,

331

bias in 254, 264, 286, 303,

305, 323, 324, 325, 326, 327, 328, 331,

335, 349

decision boundaries 307,

317, 319, 320, 326, 327, 328, 329, 334,336, 337, 339, 341, 347, 357, 359

described 61, 142, 144, 160,

168, 198, 204, 205, 207, 209, 222, 224,

231, 242, 260, 275, 285, 301, 310, 320,

322, 324, 349, 363, 367, 368, 378, 381,

396, 404, 412, 432, 454, 468, 471, 474,

475, 476, 491

effectiveness 7, 28, 33, 37,

38, 40, 42, 46, 48, 49, 68, 92, 110, 111,112, 127, 135, 158, 160, 161, 162, 163,

166, 168, 173, 177, 180, 181, 182, 185,

189, 190, 191, 192, 193, 194, 199, 200,

219, 222, 224, 232, 247, 257, 258, 260,

261, 262, 263, 272, 279, 283, 284, 289,

290, 292, 293, 295, 296, 297, 298, 301,

313, 315, 317, 320, 322, 323, 328, 331,

334, 347, 349, 350, 351, 352, 353, 354,

360, 361, 364, 365, 367, 368, 370, 381,388, 399, 420, 431, 458, 490

instance-based learning

314

memory-based learning

314

memory capaci ty 327,

335, 336

multinomial Naive Bayes vs.

256, 273as nonlinear classification

303, 305, 316, 320, 323, 324

testing/training capacity

time complexity/optimality

variance 254, 286, 303, 305,

323, 324, 325, 326, 327, 328, 331, 335,

381, 382, 383, 387, 414


57/87

549

Voronoi tessellation Voronoi

312, 329

KNN classification KNN K

nearestneighbor classification kNN K

311

Kruskals algorithm Kruskal

Kullback-Leibler divergence KL

264, 330, 387

KWIC (keyword-in-context)

L

Labeling 391, 410, 411, 412,

415

of clusters 365, 391, 392,

410, 411, 412, 415

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,

379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

Language of an automaton

250, 251, 252

Language identification 34,

48

Language issues relevance

feedback 177, 183, 184,

185, 186, 187, 188, 189, 190, 191, 192,

193, 194, 195, 200, 193, 196, 193, 200,

184, 185, 186, 187, 188, 196, 188, 189,

190, 191, 192, 193, 194, 193, 194, 195,

196, 200, 230, 234, 236, 239, 240, 241,

242, 244, 246, 230, 234, 236, 195, 240,241, 242, 244, 246, 230, 234, 236, 239,

240, 241, 242, 244, 246, 230, 234, 236,

239, 240, 241, 242, 244, 246, 262, 263,

264, 262, 263, 264, 262, 263, 264, 262,

263, 239, 309, 310, 311, 330, 309, 310,

311, 330, 309, 310, 311, 330, 309, 310,

311, 330, 194, 195, 196, 200, 194, 195,

264

Language models 67, 139,222, 226, 243, 247, 249, 250, 251, 252,

253, 254, 255, 256, 258, 260, 261, 262,

263, 264, 265, 277, 368, 382

Bayesian smoothing 265

BIM/XML vs., 230

clustering in

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

distributions multinomial

254, 256, 257, 258, 259, 260,

273, 274, 275, 276, 277, 278, 279, 280,

281, 282, 283, 284, 285, 286, 289, 290,

292, 293, 299, 300, 321, 329, 346, 347,


58/87

550

419

document likelihood

263, 264

extended approaches 119,197, 198, 201, 230, 250, 361, 432

finite automata and

250, 251

Kullback-Leibler divergence KL

264, 330, 387

likelihood ratio 252, 254,

255, 264

linear interpolation 258,

259, 265overview 19, 455, 456

query likelihood 249,

250, 255, 261, 263, 264, 432

tf-idf weighting vs. tf-idf

translation 2, 5, 35, 59, 184,

200, 253, 256, 264, 386, 450, 490

types of 2, 13, 27, 30, 39, 42,

45, 48, 69, 83, 116, 123, 160, 206, 208,209, 212, 220, 224, 237, 256, 262, 268,

269, 271, 323, 326, 348, 349, 350, 358,

359, 369, 371, 383, 392, 447, 452

Laplace smoothing 275

Latent Dirichlet Allocation (LDA) LDA

Latent semantic analysis (LSA)

92

Latent semantic indexing (LSI) 199, 368

LDA (Latent Dirichlet Allocation) LDA

L2 distance L2

Learning algorithm described

Weighted zone scoring

Learning error

Learning method 22, 42,

121, 152, 155, 174, 226, 270, 271, 272,

273, 298, 300, 316, 323, 324, 325, 326,

327, 328, 333, 334, 343, 347, 348, 350,

355, 361, 482Lemma 36, 37

Lemmatization described

Lemmatizer 37, 92

Length-normalization

Levenshtein distance Levenshtein

Lexicalized subtrees 215,

216, 225

Lexicons in inverted indexes

52Likelihood 40, 43, 53, 61, 103,

117, 134, 148, 231, 232, 236, 238, 240,

249, 250, 252, 254, 255, 256, 261, 263,

264, 274, 281, 292, 313, 322, 326, 328,

330, 381, 382, 383, 384, 387, 432, 464

Likelihood ratio 252, 254,

255, 264

Linear algebra review

Linear classifiers 303,305, 316, 317, 318, 319, 320, 321, 322,

323, 324, 326, 328, 331, 336, 344, 345,

350, 356, 357, 358

Linear interpolation 258,

259, 265

Linear problem 319, 320,

326, 342

Linear separability 317, 319,

320, 328, 330, 333, 334, 341, 344, 345,359

Link analysis 441, 467, 473,

474, 476, 489, 490, 493

anchor text 264, 411, 412,

454, 459, 474, 475, 476, 483, 489, 492

authority score authority 487,

488, 489, 490, 491, 492

ergodic Markov chain


59/87

551

HITS 474, 489, 490, 491, 493

hub score 487

Markov chains 477, 478,

479, 480, 481, 482, 484, 485, 486, 492overview 19, 455, 456

PageRank PageRank 485,

486

steady-state theorem

Link farms 493

Link spam

LLRUN 112

L M L a n g u a g e

models 67, 139, 222, 226,243, 247, 249, 250, 251, 252, 253, 254,

255, 256, 258, 260, 261, 262, 263, 264,

265, 277, 368, 382

Logarithmic merging 82, 83,

86

Lossless compression 92

Lossy compression 92

Lovins stemmer Lovins

Low-rank approximation 417, 418, 420, 422, 424, 425, 426, 427,

430, 432

LSA latent semantic analysis

92

LSI latent semantic indexing

199, 368

M

Machine-learned relevance described

Machine learning methods

22, 42, 152, 155, 174, 226, 298,

333, 334, 347, 348, 350, 355, 361, 482

Machine translation 253,

264, 386

Macroaveraging 296

MAP mean average precision

167

Map phase map

MapReduce 77, 78, 79, 80, 85, 86

Marginal relevance 222

Marginal statistic 172

Margins 83, 92, 209, 269, 323,336, 337, 338, 339, 341, 347, 463, 464,

468, 470

Markov chains 477, 478,

479, 480, 481, 482, 484, 485, 486, 492

Master node 77, 78, 79, 80

Matrix decomposition 417,

418, 420, 421, 424, 432

eigen 5, 27, 33, 137, 150, 152,

267, 271, 276, 279, 283, 284, 286, 287,288, 289, 290, 291, 292, 293, 298, 299,

300, 301, 305, 316, 319, 320, 322, 323,

327, 328, 331, 334, 336, 339, 342, 343,

344, 345, 347, 348, 351, 352, 353, 354,

355, 356, 358, 359, 360, 361, 410, 412,

418, 419, 420, 421, 422, 423, 425, 427,

434, 476, 478, 480, 488, 489, 490, 491,

492

eigenvalues 418, 419, 420,421, 422, 423, 425, 427, 478, 480, 488

Frobenius norm

latent semantic indexing

199, 368

linear algebra review

low-rank approximation

417, 418, 420, 422, 424, 425, 426, 427,

430, 432

reduced SVD SVD 423, 426singular value 419, 421,

423, 424, 425, 426, 428, 429

symmetric diagonal

422, 423

theorems 231, 233, 234, 280,

414, 421, 422, 423, 424, 425, 432, 451,

452, 480, 481

truncated SVD SVD 423,

426


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552

Maximization step M 383, 384, 387

Maximum a posteriori

Maximum likelihood estimate (MLE)

238, 240, 256, 274,384, 387

Mean average precision

167

Medoids 379, 389, 415

Memory-based learning

314

Memory capacity 327, 335,

336

Mercator crawler Mercator458, 470

Mercer kernels Mercer 346

Merge algorithm 12, 13,

14, 15, 21, 39, 40, 41, 45, 47, 86, 153,

404

Merge postings list

13, 14, 39, 40, 41, 44, 46, 47, 142, 153

Metadata 27, 116, 152, 153,

178, 388Microaveraging 296, 299,

348

Minimum spanning tree

413, 414

Minimum variance clustering

ModApte split

Model-based clustering

Model complexity 380,381

Monotonicity 393, 406, 413

Multiclass classification

321, 343

Multiclass SVMs SVM 360

Multilabel classification

322, 323, 332

Multimodal class

Multinomial classification

299

Multinomial model 254,

260, 278, 279, 280, 281, 282, 283, 284,

285, 289, 290, 292, 293, 300Multinomial Naive Bayes

256, 273

Bernoulli model 259,

260, 267, 278, 279, 280, 281, 282, 283,

284, 285, 287, 289, 290, 292, 300, 383,

384

bias in 254, 264, 286, 303,

305, 323, 324, 325, 326, 327, 328, 331,

335, 349concept drift 284, 285,

298, 301

conditional independence assumption

235, 281, 282, 283

as linear classifier

optimal classifier 316

positional independence assumption

274, 282, 283, 285,

299properties 3, 23, 30, 89, 90,

91, 94, 178, 234, 314, 323, 327, 381,

433, 436, 441, 458, 468

in query likelihood models

249, 250, 255, 261, 264, 432

random variables X and U

X U

semi-supervised learning

sparseness 7, 213, 219, 254,

257, 275, 277, 282, 352, 368

testing/training capacity

in text classification 158,

160, 161, 260, 267, 268, 269, 270, 271,

272, 273, 276, 277, 280, 284, 285, 286,

292, 293, 294, 295, 297, 298, 300, 303,

301, 304, 301, 303, 304, 305, 309, 310,


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311, 315, 316, 319, 323, 324, 325, 328,

331, 333, 334, 341, 343, 347, 348, 350,

351, 352, 353, 354, 355, 360, 361, 382

variance 254, 286, 303, 305,323, 324, 325, 326, 327, 328, 331, 335,

381, 382, 383, 387, 414

Mul t inomia l NB NB

Multinomial Naive Bayes 273, 274,

275, 277, 281, 299

Multivalue classification

321

Multivariate Bernoulli model

Mutual information 286,

287, 288, 289, 292, 294, 300, 301, 352,

354, 371, 372, 386, 411

N

Naive Bayes assumption

Naive Bayes learning method

Multinomial Naive

Bayes; Multivariate Bernoulli model

271

Named entity tagging

352

Nat iona l Ins t i tu te of S tandards and

Technology

Natural language processing

37, 178, 262

issues in 22, 37, 175, 415

lemmatizers in 35, 36, 37,

48, 92

text summarization 178,

181, 354

XML retrieval XML

Navigational queries 444,

445

NDCG (normalized discounted cumulative

gain) 169

Near-duplicate search results

Nested elements 212

NEXI 208, 209, 210, 227

Next-best merge (NBM) arrays

Next word index 46

N-gram language model N

Bigram language model

Unigram language model

Nibble 4 103,

112

NLP Natural language processing

37, 178, 262

N M I N o r m a l i z e d m u t u a linformation (NMI)

371, 372, 373, 374, 388

Noise documents 319, 326,

327, 328, 341

Noise feature 284, 286, 287,

316, 319

Nonlinear classifiers

303, 316, 320, 323, 324

Nonlinear problem 320,326, 342

Normalization

in probability theory 104,

229, 230, 231, 240, 243, 247, 257, 262

term 3, 5, 6, 7, 8, 9, 10, 11,

12, 13, 14, 15, 16, 17, 18, 21, 22, 25,

26, 27, 29, 30, 31, 32, 34, 37, 38, 40,

41, 42, 43, 44, 45, 46, 52, 53, 54, 55,

56, 57, 58, 59, 60, 61, 62, 63, 64, 65,66, 67, 72, 73, 74, 75, 76, 77, 78, 79,

80, 81, 82, 83, 84, 85, 86, 89, 90, 91,

92, 93, 94, 95, 96, 97, 98, 99, 100, 106,

107, 108, 109, 110, 111, 112, 113, 115,

116, 118, 119, 121, 123, 124, 125, 126,

127, 129, 130, 131, 132, 133, 134, 47,

135, 134, 135, 137, 138, 139, 142, 143,

144, 145, 146, 147, 149, 150, 152, 153,

154, 155, 171, 178, 179, 184, 187, 190,


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191, 192, 195, 196, 197, 198, 199, 200,

208, 210, 212, 213, 214, 215, 216, 217,

218, 219, 225, 226, 230, 233, 234, 235,

236, 237, 238, 239, 240, 241, 242, 243,244, 245, 246, 250, 251, 252, 253, 254,

256, 257, 260, 261, 262, 263, 264, 273,

274, 275, 276, 278, 279, 280, 281, 282,

283, 284, 285, 286, 287, 288, 289, 290,

291, 292, 293, 294, 298, 304, 309, 311,

314, 315, 318, 329, 343, 347, 352, 355,

359, 365, 366, 367, 368, 379, 382, 383,

384, 385, 410, 411, 412, 415, 417, 418,

422, 423, 424, 425, 427, 428, 429, 430,431, 432, 439, 444, 448, 450, 459, 466,

467, 471, 475, 476, 490

tf weighting tf 123, 131

URL 27, 434, 437, 453, 457, 458,

459, 460, 461, 462, 463, 464, 465, 466,

467, 468, 469, 470, 477

Normalized discounted cumulative gain

(NDCG)

Normalized mutual information (NMI)

Normalized tokens in inverted indexes

Normal vectors 308, 317, 335

Novelty detection 388,

409

NTCIR collection NTCIR

O

Objective function 341, 369,

370, 375, 377, 379, 380, 382

Odds 232

Odds ratio

Okapi BM25 weighting Okapi BM25

230

1/0 loss 1/0 233

One-of classification 321,

323, 331

One-versus-all (OVA) classification

343

Optimal classifier 316

Optimal clustering

Optimal learning method Optimal weight

Ordering 4, 8, 9, 10, 13, 15, 17,

18, 30, 53, 55, 58, 59, 69, 70, 72, 73,

74, 75, 76, 77, 79, 83, 84, 85, 86, 96,

98, 111, 113, 116, 119, 120, 125, 126,

130, 133, 134, 141, 142, 143, 145, 146,

147, 149, 150, 151, 153, 154, 155, 158,

167, 169, 170, 173, 177, 178, 181, 195,

205, 209, 213, 217, 223, 224, 226, 229,230, 232, 233, 234, 235, 237, 239, 246,

250, 252, 255, 256, 259, 261, 262, 264,

270, 277, 292, 322, 330, 334, 355, 357,

358, 359, 361, 368, 401, 435, 436, 442,

443, 448, 451, 453, 459, 468, 469, 474,

476, 482, 487, 490, 492

Ordinal regression 358, 361

Outliers 334, 341, 377, 378,

396, 399Out-links 438, 477, 479

Overfitting 286, 356

Overlap score measure

126

Oxford English Dictionary

92

P

PageRank 485

computation 7, 10, 12, 13, 19,

30, 31, 41, 44, 47, 48, 53, 60, 61, 62,

63, 64, 65, 67, 68, 70, 72, 75, 77, 78,

81, 85, 90, 96, 102, 106, 107, 109, 110,

111, 113, 115, 116, 118, 119, 120, 121,

122, 123, 124, 125, 126, 127, 128, 129,

130, 131, 132, 133, 134, 135, 136, 137,

138, 141, 142, 143, 144, 145, 146, 147,

148, 149, 150, 152, 153, 158, 162, 163,

164, 165, 166, 167, 168, 169, 170, 171,


63/87

555

172, 173, 175, 176, 177, 179, 180, 181,

185, 188, 190, 191, 192, 193, 195, 197,

198, 199, 213, 215, 216, 217, 218, 219,

222, 226, 230, 231, 232, 234, 235, 236,237, 239, 139, 155, 239, 237, 239, 240,

241, 242, 243, 244, 245, 246, 247, 252,

253, 254, 255, 256, 257, 258, 260, 261,

262, 263, 264, 273, 274, 275, 276, 278,

279, 280, 282, 283, 284, 285, 286, 287,

288, 290, 291, 292, 293, 294, 295, 296,

297, 299, 300, 303, 304, 305, 306, 307,

309, 311, 313, 314, 319, 322, 324, 329,

335, 336, 339, 342, 343, 345, 346, 347,352, 355, 358, 359, 364, 365, 367, 368,

369, 370, 371, 373, 374, 375, 376, 377,

378, 379, 383, 384, 386, 387, 392, 393,

395, 396, 398, 400, 401, 402, 403, 404,

405, 406, 411, 412, 413, 414, 415, 420,

422, 423, 424, 425, 426, 427, 428, 429,

430, 431, 436, 450, 451, 452, 453, 467,

474, 475, 477, 479, 480, 481, 482, 484,

485, 486, 487, 488, 489, 490, 491, 492,493

described 61, 142, 144, 160, 168,

198, 204, 205, 207, 209, 222, 224, 231,

242, 260, 275, 285, 301, 310, 320, 322,

324, 349, 363, 367, 368, 378, 381, 396,

404, 412, 432, 454, 468, 471, 474, 475,

476, 491

ergodic Markov chain

479, 480, 484Markov chains 477, 478,

479, 480, 481, 482, 484, 485, 486, 492

personalized 484, 485, 486

principal left eigen vector

478, 480

probability vectors 478, 479,

480

steady-state theorem

stochastic matrix 478, 489

teleport operation

477, 480, 481, 482, 483, 484, 485, 486,

492, 493

topic-specific 485Paice stemmer Paice

Paid inclusion 440

Parameter-free compression

Parameterized compression

Parameter tuning

Parameter tying 353

Parametric indexes 115,

116, 117Parametric search

Parser 78, 79, 80, 151, 152,

153, 199

Parsing functions, designing

Parsing modules

Partitional clustering 391,

409, 410, 415

Partition rule Passage retrieval 222, 226

Patent databases 204

Performance 18, 25, 37, 49, 68,

70, 71, 103, 112, 131, 158, 159, 160,

168, 190, 193, 194, 195, 224, 233, 242,

245, 246, 254, 258, 259, 262, 263, 265,

282, 289, 295, 298, 301, 322, 334, 348,

349, 353, 432, 457

Permuterm index 56, 57, 58,59, 62, 67

P e r s o n a l i z e d P a g e R a n k

PageRank 485, 486

Phonetic correction 66

Phrase index 43, 46

Phrase queries 17, 18, 21,

22, 27, 28, 30, 41, 42, 43, 44, 46, 47,

49, 144, 151, 154, 155, 254

Phrase search 44, 113


64/87

556

Pivoted document length normalization

Pivot length

Pointwise mutual information 287, 301

Polytomous classification

321, 343

Polytopes 312

Pooling

Pornography filtering 352

Porter stemmer Porter

38

Positional independence assumption 274, 282, 283, 285,

299

Positional indexes 43, 44,

45, 46, 47, 48, 179

Posterior probability 231,

273, 280

Postfiltering, in k-gram indexes k-gram

Postings in block sort-based indexing

compression and 7, 14, 23,

30, 40, 44, 45, 70, 71, 73, 75, 77, 83,

86, 89, 90, 91, 92, 94, 95, 96, 97, 98,

99, 100, 101, 102, 97, 98, 99, 100, 101,

102, 103, 104, 105, 106, 107, 108, 109,

110, 111, 112, 109, 112, 113, 99, 169,

468, 104, 105, 106, 107, 108, 112, 109,110, 111, 112, 113, 103

defined 2, 3, 4, 17, 22, 25, 26,

29, 42, 52, 55, 61, 62, 63, 79, 85, 90,

92, 94, 100, 104, 110, 118, 121, 125,

127, 133, 145, 160, 161, 162, 163, 165,

166, 167, 168, 170, 173, 180, 188, 195,

204, 208, 211, 213, 215, 216, 217, 219,

220, 221, 230, 234, 235, 237, 259, 262,

264, 271, 272, 276, 280, 283, 284, 287,

290, 292, 300, 301, 307, 308, 317, 318,

319, 321, 324, 325, 327, 329, 334, 335,

336, 341, 344, 345, 346, 352, 353, 358,

359, 365, 368, 369, 371, 372, 374, 377,379, 382, 383, 386, 387, 392, 393, 396,

397, 400, 404, 405, 407, 409, 412, 413,

424, 427, 437, 438, 443, 446, 450, 451,

465, 479, 480, 481, 487, 492

in inverted indexes 1, 4,

7, 8, 9, 10, 11, 17, 19, 22, 40, 48, 52,

55, 56, 57, 58, 66, 67, 70, 72, 73, 74,

75, 76, 77, 79, 82, 84, 87, 90, 92, 105,

106, 107, 108, 109, 111, 113, 117, 119,130, 142, 144, 147, 152, 153, 196, 204,

205, 218, 315, 331, 367, 368, 412, 435,

468

positional 7, 8, 10, 12, 21, 24,

29, 39, 40, 41, 43, 44, 45, 46, 47, 48,

71, 72, 80, 83, 90, 91, 92, 95, 98, 100,

113, 116, 152, 153, 165, 167, 170, 178,

179, 215, 230, 254, 274, 278, 280, 281,

282, 283, 285, 299, 318, 328, 334, 335,350, 354, 366, 376, 434, 443, 444, 461,

469, 477, 479

Postings list

compression of 7, 14, 23, 30,