信息检索入门：从经典到网页搜索

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"Introduction to Information Retrieval" 《Introduction to Information Retrieval》是一本全面介绍信息检索领域的教科书，由Christopher D. Manning、Prabhakar Raghavan和Hinrich Schütze三位专家共同撰写。这本书是首个将传统信息检索与网络信息检索（包括网页搜索）、文本分类和文本聚类等主题统一处理的教材。它从计算机科学的角度出发，对文档收集、索引、搜索系统的设计与实现的各个方面进行了最新、最全面的阐述，并且引入了在文本集合上应用机器学习方法的概念。书中详细探讨了以下核心知识点： 1. **信息检索基础**：涵盖了信息检索的基本概念，如查询分析、文档表示（如TF-IDF）、布尔模型和概率检索模型。 2. **文本分类**：讲解如何利用统计和机器学习方法对文本进行自动分类，如朴素贝叶斯分类器、支持向量机（SVM）及其在信息检索中的应用。 3. **文本聚类**：介绍无监督学习方法，用于将相似文档分组，如K-means聚类和层次聚类算法。 4. **搜索引擎技术**：深入讨论搜索引擎的工作原理，包括爬虫技术、网页抓取、网页解析（如HTML解析）、倒排索引的构建和维护。 5. **检索评估**：解释信息检索系统的评价标准，如查准率、查全率、F1分数以及TREC评估方法等。 6. **用户交互与个性化**：探讨如何理解用户的查询意图，改进检索结果排序，以及如何通过用户行为数据实现个性化搜索。 7. **机器学习在信息检索中的应用**：介绍深度学习和神经网络模型在信息检索中的新进展，如词嵌入（Word Embeddings）、循环神经网络（RNNs）和Transformer模型在NLP任务中的应用。 8. **课程配套资源**：本书提供了完整的教学课件和习题集，便于教师教学和学生学习。这本书不仅适合计算机科学和信息科学的研究生或高年级本科生作为教材使用，也是研究人员和业界专业人士深入理解信息检索理论和技术的重要参考资料。通过学习，读者可以掌握构建高效、准确的信息检索系统所需的关键技术和方法。

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xvi Preface

exponential growth in size. But major scientiﬁc innovations, superb engi-

neering, the rapidly declining price of computer hardware, and the rise of

acommercialunderpinningforwebsearchhaveallconspiredtopowerto-

day’s major search engines, which are able to provide high-quality results

within subsecond response times for hundreds of millions of searches a day

over billions of web pages.

Book organization and course development

This book is the result of a series of courses we have taught at Stanford Uni-

versity and at the University of Stuttgart, in a range of durations including

asinglequarter,onesemester,andtwoquarters.Thesecourseswereaimed

at early stage graduate students in computer science, but we have also had

enrollment from upper-class computer science undergraduates, as well as

students from law, medical informatics, statistics, linguistics, and various en-

gineering disciplines. The key design principle for this book, therefore, was

to cover what we believe to be important in a one-term graduate course on

IR. An additional principle is to build each chapter around material that we

believe can be covered in a single lecture of 75 to 90 minutes.

The ﬁrst eight chapters of the book are devoted to the basics of information

retrieval and in particular the heart of search engines; we consider this ma-

terial to be core to any course on information retrieval. Chapter 1 introduces

inverted indexes and shows how simple Boolean queries can be processed

using such indexes. Chapter 2 builds on this introduction by detailing the

manner in which documents are preprocessed before indexing and by dis-

cussing how inverted indexes are augmented in various ways for function-

ality and speed. Chapter 3 discusses search structures for dictionaries and

how to process queries that have spelling errors and other imprecise matches

to the vocabulary in the document collection being searched. Chapter 4 de-

scribes a number of algorithms for constructing the inverted index from a

text collection with particular attention to highly scalable and distributed al-

gorithms that can be applied to very large collections. Chapter 5 covers tech-

niques for compressing dictionaries and inverted indexes. These techniques

are critical for achieving subsecond response times to user queries in large

search engines. The indexes and queries considered in Chapters 1 through 5

only deal with Boolean retrieval,inwhichadocumenteithermatchesaquery

or does not. A desire to measure the extent to which a document matches a

query, or the score of a document for a query, motivates the development of

term weighting and the computation of scores in Chapters 6 and 7, leading

to the idea of a list of documents that are rank-ordered for a query. Chapter 8

focuses on the evaluation of an information retrieval system based on the

relevance of the documents it retrieves, allowing us to compare the relative

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performances of different systems on benchmark document collections and

queries.

Chapters 9 through 21 build on the foundation of the ﬁrst eight chapters

to cover a variety of more advanced topics. Chapter 9 discusses methods by

which retrieval can be enhanced through the use of techniques like relevance

feedback and query expansion, which aim at increasing the likelihood of re-

trieving relevant documents. Chapter 10 considers IR from documents that

are structured with markup languages like XML and HTML. We treat struc-

tured retrieval by reducing it to the vector space scoring methods developed

in Chapter 6. Chapters 11 and 12 invoke probability theory to compute scores

for documents on queries. Chapter 11 develops traditional probabilistic IR,

which provides a framework for computing the probability of relevance of

adocument,givenasetofqueryterms.Thisprobabilitymaythenbeused

as a score in ranking. Chapter 12 illustrates an alternative, wherein, for each

document in a collection, we build a language model from which one can

estimate a probability that the language model generates a given query. This

probability is another quantity with which we can rank-order documents.

Chapters 13 through 18 give a treatment of various forms of machine learn-

ing and numerical methods in information retrieval. Chapters 13 through 15

treat the problem of classifying documents into a set of known categories,

given a set of documents along with the classes they belong to. Chapter 13

motivates statistical classiﬁcation as one of the key technologies needed for

asuccessfulsearchengine;introducesNaiveBayes,aconceptuallysimple

and efﬁcient text classiﬁcation method; and outlines the standard method-

ology for evaluating text classiﬁers. Chapter 14 employs the vector space

model from Chapter 6 and introduces two classiﬁcation methods, Rocchio

and k nearest neighbor (kNN), that operate on document vectors. It also

presents the bias-variance tradeoff as an important characterization of learn-

ing problems that provides criteria for selecting an appropriate method for a

text classiﬁcation problem. Chapter 15 introduces support vector machines,

which many researchers currently view as the most effective text classiﬁca-

tion method. We also develop connections in this chapter between the prob-

lem of classiﬁcation and seemingly disparate topics such as the induction of

scoring functions from a set of training examples.

Chapters 16, 17, and 18 consider the problem of inducing clusters of related

documents from a collection. In Chapter 16, we ﬁrst give an overview of a

number of important applications of clustering in IR. We then describe two

ﬂat clustering algorithms: the K-means algorithm, an efﬁcient and widely

used document clustering method, and the expectation-maximization al-

gorithm, which is computationally more expensive, but also more ﬂexible.

Chapter 17 motivates the need for hierarchically structured clusterings (in-

stead of ﬂat clusterings) in many applications in IR and introduces a number

of clustering algorithms that produce a hierarchy of clusters. The chapter

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xviii Preface

also addresses the difﬁcult problem of automatically computing labels for

clusters. Chapter 18 develops methods from linear algebra that constitute

an extension of clustering and also offer intriguing prospects for algebraic

methods in IR, which have been pursued in the approach of latent semantic

indexing.

Chapters 19 through 21 treat the problem of web search. We give in Chap-

ter 19 a summary of the basic challenges in web search, together with a set

of techniques that are pervasive in web information retrieval. Next, Chap-

ter 20 describes the architecture and requirements of a basic web crawler.

Finally, Chapter 21 considers the power of link analysis in web search, using

in the process several methods from linear algebra and advanced probability

theory.

This book is not comprehensive in covering all topics related to IR. We

have put aside a number of topics, which we deemed outside the scope of

what we wished to cover in an introduction to IR class. Nevertheless, for

people interested in these topics, we provide the following pointers to mainly

textbook coverage:

Cross-language IR Grossman and Frieder 2004, ch. 4, and Oard and

Dorr 1996.

Image and multimedia IR Grossman and Frieder 2004, ch. 4; Baeza-

Yates and Ribeiro-Neto 1999, ch. 6; Baeza-Yates and Ribeiro-Neto 1999,

ch. 11; Baeza-Yates and Ribeiro-Neto 1999, ch. 12; del Bimbo 1999; Lew

2001; and Smeulders et al. 2000.

Speech retrieval Coden et al. 2002.

Music retrieval Downie 2006 and

http://www.ismir.net/.

User interfaces for IR Baeza-Yates and Ribeiro-Neto 1999, ch. 10.

Parallel and peer-to-peer IR Grossman and Frieder 2004, ch. 7; Baeza-

Yates and Ribeiro-Neto 1999, ch. 9; and Aberer 2001.

Digital libraries Baeza-Yates and Ribeiro-Neto 1999, ch. 15, and Lesk

2004.

Information science perspective Korfhage 1997; Meadow et al. 1999;

and Ingwersen and J

arvelin 2005.

Logic-based approaches to IR van Rijsbergen 1989.

Natural language processing techniques Manning and Sch

utze 1999;

Jurafsky and Martin 2008; and Lewis and Jones 1996.

Prerequisites

Introductory courses in data structures and algorithms, in linear algebra, and

in probability theory sufﬁce as prerequisites for all twenty-one chapters. We

now give more detail for the beneﬁt of readers and instructors who wish to

tailor their reading to some of the chapters.

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Preface xix

Chapters 1 through 5 assume as prerequisite a basic course in algorithms

and data structures. Chapters 6 and 7 require, in addition, a knowledge of

basic linear algebra, including vectors and dot products. No additional pre-

requisites are assumed until Chapter 11, for which a basic course in prob-

ability theory is required; Section 11.1 gives a quick review of the concepts

necessary in Chapters 11, 12, and 13. Chapter 15 assumes that the reader

is familiar with the notion of nonlinear optimization, although the chapter

may be read without detailed knowledge of algorithms for nonlinear op-

timization. Chapter 18 demands a ﬁrst course in linear algebra, including

familiarity with the notions of matrix rank and eigenvectors; a brief review

is given in Section 18.1. The knowledge of eigenvalues and eigenvectors is

also necessary in Chapter 21.

Book layout

✎

Worked examples in the text appear with a pencil sign next to them in the

left margin. Advanced or difﬁcult material appears in sections or subsections

indicated with scissors in the margin. Exercises are marked in the margin

✄

with a question mark. The level of difﬁculty of exercises is indicated as easy

[⋆], medium [⋆⋆], or difﬁcult [⋆⋆⋆].

Acknowledgments

The authors thank Cambridge University Press for allowing us to make the

draft book available online, which facilitated much of the feedback we have

received while writing the book. We also thank Lauren Cowles, who has been

an outstanding editor, providing several rounds of comments on each chap-

ter; on matters of style, organization, and coverage; as well as detailed com-

ments on the subject matter of the book. To the extent that we have achieved

our goals in writing this book, she deserves an important part of the credit.

We are very grateful to the many people who have given us comments,

suggestions, and corrections based on draft versions of this book. We thank

for providing various corrections and comments: Cheryl Aasheim, Josh At-

tenberg, Luc B

elanger, Tom Breuel, Daniel Burckhardt, Georg Buscher, Fa-

zli Can, Dinquan Chen, Ernest Davis, Pedro Domingos, Rodrigo Panchiniak

Fernandes, Paolo Ferragina, Norbert Fuhr, Vignesh Ganapathy, Elmer Gar-

duno, Xiubo Geng, David Gondek, Sergio Govoni, Corinna Habets, Ben

Handy, Donna Harman, Benjamin Haskell, Thomas H

uhn, Deepak Jain,

Ralf Jankowitsch, Dinakar Jayarajan, Vinay Kakade, Mei Kobayashi, Wes-

sel Kraaij, Rick Laﬂeur, Florian Laws, Hang Li, David Mann, Ennio Masi,

Frank McCown, Paul McNamee, Sven Meyer zu Eissen, Alexander Murzaku,

Gonzalo Navarro, Scott Olsson, Daniel Paiva, Tao Qin, Megha Raghavan,

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xx Preface

Ghulam Raza, Michal Rosen-Zvi, Klaus Rothenh

ausler, Kenyu L. Run-

ner, Alexander Salamanca, Grigory Sapunov, Tobias Scheffer, Nico Schlae-

fer, Evgeny Shadchnev, Ian Soboroff, Benno Stein, Marcin Sydow, Andrew

Turner, Jason Utt, Huey Vo, Travis Wade, Mike Walsh, Changliang Wang,

Renjing Wang, and Thomas Zeume.

Many people gave us detailed feedback on individual chapters, either

at our request or through their own initiative. For this, we’re particularly

grateful to James Allan, Omar Alonso, Ismail Sengor Altingovde, Vo Ngoc

Anh, Roi Blanco, Eric Breck, Eric Brown, Mark Carman, Carlos Castillo,

Junghoo Cho, Aron Culotta, Doug Cutting, Meghana Deodhar, Susan Du-

mais, Johannes F

urnkranz, Andreas Heß, Djoerd Hiemstra, David Hull,

Thorsten Joachims, Siddharth Jonathan J. B., Jaap Kamps, Mounia Lal-

mas, Amy Langville, Nicholas Lester, Dave Lewis, Stephen Liu, Daniel

Lowd, Yosi Mass, Jeff Michels, Alessandro Moschitti, Amir Najmi, Marc

Najork, Giorgio Maria Di Nunzio, Paul Ogilvie, Priyank Patel, Jan Peder-

sen, Kathryn Pedings, Vassilis Plachouras, Daniel Ramage, Stefan Riezler,

Michael Schiehlen, Helmut Schmid, Falk Nicolas Scholer, Sabine Schulte

im Walde, Fabrizio Sebastiani, Sarabjeet Singh, Alexander Strehl, John Tait,

Shivakumar Vaithyanathan, Ellen Voorhees, Gerhard Weikum, Dawid Weiss,

Yiming Yang, Yisong Yue, Jian Zhang, and Justin Zobel.

And ﬁnally there were a few reviewers who absolutely stood out in terms

of the quality and quantity of comments that they provided. We thank them

for their signiﬁcant impact on the content and structure of the book. We ex-

press our gratitude to Pavel Berkhin, Stefan B

uttcher, Jamie Callan, Byron

Dom, Torsten Suel, and Andrew Trotman.

Parts of the initial drafts of Chapters 13, 14, and 15 were based on slides

that were generously provided by Ray Mooney. Although the material has

gone through extensive revisions, we gratefully acknowledge Ray’s contri-

bution to the three chapters in general and to the description of the time

complexities of text classiﬁcation algorithms in particular.

The above is unfortunately an incomplete list; we are still in the process of

incorporating feedback we have received. And, like all opinionated authors,

we did not always heed the advice that was so freely given. The published

versions of the chapters remain solely the responsibility of the authors.

The authors thank Stanford University and the University of Stuttgart for

providing a stimulating academic environment for discussing ideas and the

opportunity to teach courses from which this book arose and in which its

contents were reﬁned. CM thanks his family for the many hours they’ve let

him spend working on this book and hopes he’ll have a bit more free time on

weekends next year. PR thanks his family for their patient support through

the writing of this book and is also grateful to Yahoo! Inc. for providing a

fertile environment in which to work on this book. HS would like to thank

his parents, family, and friends for their support while writing this book.

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信息检索入门：从经典到网页搜索

《Introduction to Information Retrieval》中爬虫课件

introduction to information retrieval

An Introduction to Information Retrieval

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请提取这篇文献An Approach to Preprocessing and Cleaning GeoNames Data for Geographic Information Retrieval的Introduction的原始内容

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information retrieval的特点

与自然语言处理有关的参考文献

Automated information retrieval systems are used to increase what has been called information overload.

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