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E-raamat: Introduction to Information Retrieval

(Universität Stuttgart), , (Stanford University, California)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Jul-2008
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9780511410802
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Jul-2008
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9780511410802
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Class-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.

Class-tested and up-to-date textbook for introductory courses on information retrieval.

Arvustused

'This is the first book that gives you a complete picture of the complications that arise in building a modern web-scale search engine. You'll learn about ranking SVMs, XML, DNS, and LSI. You'll discover the seedy underworld of spam, cloaking, and doorway pages. You'll see how MapReduce and other approaches to parallelism allow us to go beyond megabytes and to efficiently manage petabytes.' Peter Norvig, Director of Research, Google Inc. ' this book sets a high standard ' Natural Language Engineering 'Introduction to Information Retrieval is a comprehensive, authoritative, and well-written overview of the main topics in IR. The book offers a good balance of theory and practice, and is an excellent self-contained introductory text for those new to IR.' Computational Linguistics 'This book provides what Salton and Van Rijsbergen both failed to achieve Even more important, unlike some other books in IR, the authors appear to care about making the theory as accessible as possible to the reader, on occasion including short primers to certain topics or choosing to explain difficult concepts using simplified approaches. its coverage [ is] excellent, the quality of writing high and I was surprised how much I learned from reading it. I think the online resources are impressive.' Natural Language Engineering

Muu info

A class-tested and up-to-date textbook for introductory courses on information retrieval.
Table of Notation
xi
Preface xv
Boolean retrieval
1(17)
An example information retrieval problem
3(3)
A first take at building an inverted index
6(3)
Processing Boolean queries
9(4)
The extended Boolean model versus ranked retrieval
13(3)
References and further reading
16(2)
The term vocabulary and posting lists
18(27)
Document delineation and character sequence decoding
18(3)
Determining the vocabulary of terms
21(12)
Faster posting list intersection via skip pointers
33(3)
Positional postings and phrase queries
36(7)
References and further reading
43(2)
Dictionaries and tolerant retrieval
45(16)
Search structures for dictionaries
45(3)
Wildcard queries
48(4)
Spelling correction
52(6)
Phonetic correction
58(1)
References and further reading
59(2)
Index construction
61(17)
Hardware basics
62(1)
Blocked sort-based indexing
63(3)
Single-pass in memory indexing
66(2)
Distributed indexing
68(3)
Dynamic indexing
71(2)
Other types of indexes
73(3)
References and further reading
76(2)
Index compression
78(22)
Statistical properties of terms in information retrieval
79(3)
Dictionary compression
82(5)
Posting file compression
87(10)
References and further reading
97(3)
Scoring, term weighting, and the vector space model
100(24)
Parametric and zone indexes
101(6)
Term frequency and weighting
107(3)
The vector space model for scoring
110(6)
Variant tf-idf functions
116(6)
References and further reading
122(2)
Computing scores in a complete search system
124(15)
Efficient scoring and ranking
124(8)
Components of an information retrieval system
132(4)
Vector space scoring and query operator interaction
136(1)
References and further reading
137(2)
Evaluation in information retrieval
139(23)
Information retrieval system evaluation
140(1)
Standard test collections
141(1)
Evaluation of unranked retrieval sets
142(3)
Evaluation of ranked retrieval results
145(6)
Assessing relevance
151(3)
A broader perspective: System quality and user utility
154(3)
Results snippets
157(2)
References and further reading
159(3)
Relevance feedback and query expansion
162(16)
Relevance feedback and pseudo relevance feedback
163(10)
Global methods for query reformulation
173(4)
References and further reading
177(1)
XML retrieval
178(23)
Basic XML concepts
180(3)
Challenges in XML retrieval
183(5)
A vector space model for XML retrieval
188(4)
Evaluation of XML retrieval
192(4)
Text-centric versus data-centric XML retrieval
196(2)
References and further reading
198(3)
Probabilistic information retrieval
201(17)
Review of basic probability theory
202(1)
The probability ranking principle
203(1)
The binary independence model
204(8)
An appraisal and some extensions
212(4)
References and further reading
216(2)
Language models for information retrieval
218(16)
Language models
218(5)
The query likelihood model
223(6)
Language modeling versus other approaches in information retrieval
229(1)
Extended language modeling approaches
230(2)
References and further reading
232(2)
Text classification and Naive Bayes
234(32)
The text classification problem
237(1)
Naive Bayes text classification
238(5)
The Bernoulli model
243(2)
Properties of Naive Bayes
245(6)
Feature selection
251(7)
Evaluation of text classification
258(6)
References and further reading
264(2)
Vector space classification
266(27)
Document representations and measures of relatedness in vector spaces
267(2)
Rocchio classification
269(4)
k nearest neighbor
273(4)
Linear versus nonlinear classifiers
277(4)
Classification with more than two classes
281(3)
The bias-variance tradeoff
284(7)
References and further reading
291(2)
Support vector machines and machine learning on documents
293(28)
Support vector machines: The linearly separable case
294(6)
Extensions to the support vector machine model
300(7)
Issues in the classification of text documents
307(7)
Machine-learning methods in ad hoc information retrieval
314(4)
References and further reading
318(3)
Flat clustering
321(25)
Clustering in information retrieval
322(4)
Problem statement
326(1)
Evaluation of clustering
327(4)
K-means
331(7)
Model-based clustering
338(5)
References and further reading
343(3)
Hierarchical clustering
346(23)
Hierarchical agglomerative clustering
347(3)
Single-link and complete-link clustering
350(6)
Group-average agglomerative clustering
356(2)
Centroid clustering
358(2)
Optimality of hierarchical agglomerative clustering
360(2)
Divisive clustering
362(1)
Cluster labeling
363(2)
Implementation notes
365(2)
References and further reading
367(2)
Matrix decompositions and latent semantic indexing
369(16)
Linear algebra review
369(4)
Term-document matrices and singular value decompositions
373(3)
Low-rank approximations
376(2)
Latent semantic indexing
378(5)
References and further reading
383(2)
Web search basics
385(20)
Background and history
385(2)
Web characteristics
387(5)
Advertising as the economic model
392(3)
The search user experience
395(1)
Index size and estimation
396(4)
Near-duplicates and shingling
400(4)
References and further reading
404(1)
Web crawling and indexes
405(16)
Overview
405(1)
Crawling
406(9)
Distributing indexes
415(1)
Connectivity servers
416(3)
References and further reading
419(2)
Link analysis
421(20)
The Web as a graph
422(2)
PageRank
424(9)
Hubs and authorities
433(6)
References and further reading
439(2)
Bibliography 441(28)
Index 469
Christopher Manning is an Associate Professor of Computer Science and Linguistics at Stanford University. His research concentrates on probabilistic models of language and statistical natural language processing, information extraction, text understanding and text mining. Dr Prabhakar Raghavan is Head of Yahoo! Research and a Consulting Professor of Computer Science at Stanford University. Dr Hinrich Schütze resides as Chair of Theoretical Computational Linguistics at the Institute for Natural Language Processing, University of Stuttgart.