TY  - BOOK
AU  - Manning, Christopher D.
AU  - Raghava, Prabhakar
AU  - Schütze, Hinrich
TI  - Introduction to Information Retrieval 
SN  - 9781107666399 (pb)
U1  - 025.04 
PY  - 2008///
CY  - New York
PB  - Cambridge University Press
KW  - Text processing (Computer science)
KW  - Information retrieval
KW  - Document clustering
KW  - Semantic Web
N1  - 1 Boolean retrieval 1
1.1 An example information retrieval problem 3
1.2 A first take at building an inverted index 6
1.3 Processing Boolean queries 9
1.4 The extended Boolean model versus ranked retrieval 13
1.5 References and further reading 16
2 The term vocabulary and postings lists 18
2.1 Document delineation and character sequence decoding 18
2.2 Determining the vocabulary of terms 21
2.3 Faster postings list intersection via skip pointers 33
2.4 Positional postings and phrase queries 36
2.5 References and further reading 43
3 Dictionaries and tolerant retrieval 45
3.1 Search structures for dictionaries 45
3.2 Wildcard queries 48
3.3 Spelling correction 52
3.4 Phonetic correction 58
3.5 References and further reading , 59
4 Index construction 61
4.1 Hardware basics 62
4.2 Blocked sort-based indexing 63
4.3 Single-pass in-memory indexing 66.
4.4 Distributed indexing 68
4.5 Djmamic indexing 71
4.6 Other types of indexes
4.7 References and further reading 76
5 Index compression 78
5.1 Statistical properties of terms in information retrieval 79
5.2 Dictionary compression 82
5.3 Postings file compression 87
5.4 References and further reading ^7
6 Scoring, term weighting, and the vector space model 100
6.1 Parametric and zone indexes 101
6.2 Term frequency and weighting 107
6.3 The vector space model for scoring HO
6.4 Variant tf-idf functions 118
6.5 References and further reading 122
7 Computing scores in a complete search system 124
7.1 Efficient scoring and ranking 124
7.2 Components of an information retrieval system 132
7.3 Vector space scoring and query operator interaction 136
7.4 References and further reading 137
8 Evaluation in information retrieval 139
8.1 Information retrieval system evaluation 140
8.2 Standard test collections 141
8.3 Evaluation of unranked retrieval sets 142
8.4 Evaluation of ranked retrieval results 145
8.5 Assessing relevance 1^1
8.6 A broader perspective: System quality and user
utility
8.7 Results snippets
8.8 References and further reading 139
9 Relevance feedback and query expansion 182
9.1 Relevance feedback and pseudo relevance
feedback
9.2 Globed methods for query reformulation 173
9.3 References arid further reading 177
10 XML retrieval 178
10.1 Basic XML concepts 180
10.2 Challenges in XML retrieval 183
10.3 A vector space model for XML retrieval 188
10.4 Evaluation of XML retrieval 192
154
157
10.5 Text-centric versus data-centric XML retrieval 196
10.6 References and further reading 198
U Probabilistic information retrieval 201
11.1 Review of basic probability theory 202
11.2 The probability ranking principle 203
11.3 The binary independence model 204
11.4 An appraisal and some extensions 212
11.5 References and further reading 216
12 Language models for information retrieval 218
12.1 Language models 218
12.2 The query likelihood model 223
12.3 Language modeling versus other approaches
in information retrieval 229
12.4 Extended language modeling approaches 230
12.5 References and further reading 232
13 Text classification and Naive Bayes 234
13.1 The text classification problem 237
13.2 Naive Bayes text classification 238
13.3 The Bernoulli model 243
13.4 Properties of Naive Bayes 245
13.5 Feature selection 251
13.6 Evaluation of text classification 258
13.7 References and further reading 264
14 Vector space classification 266
14.1 Ctocument representations and measiues of relatedness
in vector spaces 267
14.2 Rocchio classification 269
14.3 k nearest neighbor 273
14.4 linear versus nonlinear classifiers 277
14.5 Classification with more than two classes 281
14.6 The bias-variance tradeoff 284
14.7 References and further reading 291
15 Support vector machines and machine learning on documents 293
15.1 Support vector machines: The linearly separable case 294
15.2 Extensions to the support vector machine model 300
15.3 Issues in the classification of text documents 307
15.4 Machine-learning methods in ad hoc information retrieval 314
15.5 References and further reading 318
16 Flat clustering 321
16.1 Clustering in information retrieval 322
16.2 Problem statement 326
16.3 Evaluation of clustering 327
16.4 fC-means 331
16.5 Model-based clustering 338
16.6 References and further reading 343
17 Hierarchical clustering ■ 346
17.1 Hierarchical agglomerative clustering 347
17.2 Single-link and complete-link clustering 350
17.3 Group-average agglomerative clustering 356
17.4 Centroid clustering 358
17.5 Optimality of hierarchical agglomerative
clustering 360
17.6 Divisive clustering 362
17.7 Cluster labeling 363
17.8 Implementation notes 365
17.9 References and further reading 367
18 Matrix decompositions and latent semantic indexing 369
18.1 Linear algebra review 369
18.2 Term-document matrices and singular value
decompositions 373
18.3 Low-rank approximations 376
18.4 Latent semantic indexing 378
18.5 References and further reading 383
19 Web search basics 385
19.1 Backgroxmd and history 385
19.2 Web characteristics 387
19.3 Advertising as the economic model 392
19.4 The search user experience 395
19.5 Index size and estimation 396
19.6 Near-duplicates and shingling 400
19.7 References and further reading 404
20 Web crawling and indexes 405
20.1 Overview 405
20.2 Crawling 406
20.3 Distributing indexes 415
20.4 Connectivity servers 41b
20.5 References and further reading 41
21 Link analysis 421
21.1 The Web as a graph 422
21.2 PageRank 424
21.3 Hubs and authorities 433
21.4 References and futher reading 439
ER  -