Introduction to Information Retrieval
- New York : Cambridge University Press, 2008.
- xxi, 482 p. : ill. ; 27 cm.
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
9781107666399 (pb)
Text processing (Computer science) Information retrieval. Document clustering. Semantic Web.