Introduction to information retrieval

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Introduction to information retrieval / Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze

資料種別:

図書

出版情報:

New York : Cambridge University Press, 2008

形態:

xxi, 482 p. ; 27 cm

著者名:

ISBN:

9780521865715 [0521865719]

書誌ID:

BA8661842X

子書誌情報

フルテキスト

所蔵情報

他の版・巻

書誌詳細

注記:

Includes bibliographical references and index

主題:

Document clustering; Information retrieval; Semantic Web; Text processing (Computer science)

言語:

英語

目次情報:

Table of Notation

Preface

Boolean retrieval / 1：

An example information retrieval problem / 1.1：

A first take at building an inverted index / 1.2：

Processing Boolean queries / 1.3：

The extended Boolean model versus ranked retrieval / 1.4：

References and further reading / 1.5：

The term vocabulary and postings lists / 2：

Document delineation and character sequence decoding / 2.1：

Determining the vocabulary of terms / 2.2：

Faster postings list intersection via skip pointers / 2.3：

Positional postings and phrase queries / 2.4：

Dictionaries and tolerant retrieval / 2.5：

Search structures for dictionaries / 3.1：

Wildcard queries / 3.2：

Spelling correction / 3.3：

Phonetic correction / 3.4：

Index construction / 3.5：

Hardware basics / 4.1：

Blocked sort-based indexing / 4.2：

Single-pass in-memory indexing / 4.3：

Distributed indexing / 4.4：

Dynamic indexing / 4.5：

Other types of indexes / 4.6：

Index compression / 4.7：

Statistical properties of terms in information retrieval / 5.1：

Dictionary compression / 5.2：

Postings file compression / 5.3：

Scoring, term weighting, and the vector space model / 5.4：

Parametric and zone indexes / 6.1：

Term frequency and weighting / 6.2：

The vector space model for scoring / 6.3：

Variant tf-idf functions / 6.4：

Computing scores in a complete search system / 6.5：

Efficient scoring and ranking / 7.1：

Components of an information retrieval system / 7.2：

Vector space scoring and query operator interaction / 7.3：

Evaluation in information retrieval / 7.4：

Information retrieval system evaluation / 8.1：

Standard test collections / 8.2：

Evaluation of unranked retrieval sets / 8.3：

Evaluation of ranked retrieval results / 8.4：

Assessing relevance / 8.5：

A broader perspective: System quality and user utility / 8.6：

Results snippets / 8.7：

Relevance feedback and query expansion / 8.8：

Relevance feedback and pseudo relevance feedback / 9.1：

Global methods for query reformulation / 9.2：

XML retrieval / 9.3：

Basic XML concepts / 10.1：

Challenges in XML retrieval / 10.2：

A vector space model for XML retrieval / 10.3：

Evaluation of XML retrieval / 10.4：

Text-centric versus data-centric XML retrieval / 10.5：

Probabilistic information retrieval / 10.6：

Review of basic probability theory / 11.1：

The probability ranking principle / 11.2：

The binary independence model / 11.3：

An appraisal and some extensions / 11.4：

Language models for information retrieval / 11.5：

Language models / 12.1：

The query likelihood model / 12.2：

Language modeling versus other approaches in information retrieval / 12.3：

Extended language modeling approaches / 12.4：

Text classification and Naive Bayes / 12.5：

The text classification problem / 13.1：

Naive Bayes text classification / 13.2：

The Bernoulli model / 13.3：

Properties of Naive Bayes / 13.4：

Feature selection / 13.5：

Evaluation of text classification / 13.6：

Vector space classification / 13.7：

Document representations and measures of relatedness in vector spaces / 14.1：

Rocchio classification / 14.2：

k nearest neighbor / 14.3：

Linear versus nonlinear classifiers / 14.4：

Classification with more than two classes / 14.5：

The bias-variance tradeoff / 14.6：

Support vector machines and machine learning on documents / 14.7：

Support vector machines: The linearly separable case / 15.1：

Extensions to the support vector machine model / 15.2：

Issues in the classification of text documents / 15.3：

Machine-learning methods in ad hoc information retrieval / 15.4：

Flat clustering / 15.5：

Clustering in information retrieval / 16.1：

Problem statement / 16.2：

Evaluation of clustering / 16.3：

K-means / 16.4：

Model-based clustering / 16.5：

Hierarchical clustering / 16.6：

Hierarchical agglomerative clustering / 17.1：

Single-link and complete-link clustering / 17.2：

Group-average agglomerative clustering / 17.3：

Centroid clustering / 17.4：

Optimality of hierarchical agglomerative clustering / 17.5：

Divisive clustering / 17.6：

Cluster labeling / 17.7：

Implementation notes / 17.8：

Matrix decompositions and latent semantic indexing / 17.9：

Linear algebra review / 18.1：

Term-document matrices and singular value decompositions / 18.2：

Low-rank approximations / 18.3：

Latent semantic indexing / 18.4：

Web search basics / 18.5：

Background and history / 19.1：

Web characteristics / 19.2：

Advertising as the economic model / 19.3：

The search user experience / 19.4：

Index size and estimation / 19.5：

Near-duplicates and shingling / 19.6：

Web crawling and indexes / 19.7：

Overview / 20.1：

Crawling / 20.2：

Distributing indexes / 20.3：

Connectivity servers / 20.4：

Link analysis / 20.5：

The Web as a graph / 21.1：

PageRank / 21.2：

Hubs and authorities / 21.3：

Bibliography / 21.4：

Index

Table of Notation

Preface

Boolean retrieval / 1：

An example information retrieval problem / 1.1：

A first take at building an inverted index / 1.2：

Processing Boolean queries / 1.3：

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