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37983 results in Cambridge Textbooks

Page 1708 of 1900

  • Preface

  • Glenn Gamst, Lawrence S. Meyers, California State University, Sacramento, A. J. Guarino, Auburn University, Alabama
  • Book:
    Analysis of Variance Designs
    Published online:
    05 June 2012
    Print publication:
    01 September 2008, pp xiii-xvi

  • Summary

    TO THE INSTRUCTOR

    The present text is an exploration of univariate methodology where the effects of one or more independent variables are assessed on a single dependent variable. Such univariate designs are ubiquitous in the social, behavioral, and biological science literature. We have chosen, in this book, to focus our efforts on analysis of variance (ANOVA). Issues concerning multivariate methodology, including multiple regression analysis, are not covered in the present text as a result of space limitations, but they are addressed in a companion text (see Meyers, Gamst, & Guarino, 2006).

    This book owes both a conceptual and computational debt to early ANOVA pioneers, beginning with the seminal work of Fisher (1925, 1935), who focused on solving agricultural problems with experimental methods. Fisher's early work was adapted to other fields, including the social and behavioral sciences, and in doing so moved from childhood to early adolescence with the work of Baxter (1940, 1941), Crutchfield (1938), Garrett and Zubin (1943), Lindquist (1940), Snedecor (1934), and Yates (1937). By the 1950s, ANOVA procedures were well established within most social and behavioral sciences (e.g., Cochran & Cox, 1957; Lindquist, 1953; Scheffé, 1959).

    Beginning in the early 1960s, ANOVA procedures were further delineated and popularized by Winer (1962, 1971) and Winer, Brown, and Michels (1991). These works, while sometimes challenging to read, were considered the “gold standard” by many ANOVA practitioners.

  • 3 - ELEMENTS OF ANOVA

  • from SECTION 2 - FOUNDATIONS OF ANALYSIS OF VARIANCE
  • Glenn Gamst, Lawrence S. Meyers, California State University, Sacramento, A. J. Guarino, Auburn University, Alabama
  • Book:
    Analysis of Variance Designs
    Published online:
    05 June 2012
    Print publication:
    01 September 2008, pp 23-33

  • Summary

    PARTITIONING OF THE VARIANCE

    The term analysis of variance is very descriptive of the process we use in the statistical treatment of the data. In a general sense, to analyze something is to examine the individual elements of a whole. In ANOVA, we start with a totality and break it apart – partition it – into portions that we then examine separately.

    The totality that we break apart in ANOVA, as you might suspect from the name of the procedure, is the variance of the scores on the dependent variable. As you recall from Chapter 2, variance is equal to the sum of squares – the sum of the squared deviations from the mean – divided by the degrees of freedom. In an ANOVA design, it is the variance of the total set of scores that is being partitioned. The various ANOVA designs are different precisely because they allow this total variance to be partitioned in different ways.

    ANOVA is a statistical procedure that allows us to partition (divide) the total variance measured in the study into its sources or component parts. This total measured variance is the variance of the scores that we have obtained when participants were measured on the dependent variable. Therefore, whenever we talk about the variance associated with a given partition or whenever we talk about the total variance, we are always referring to the variance of the dependent variable.

  • 11 - TWO-WAY WITHIN-SUBJECTS DESIGN

  • from SECTION 4 - WITHIN-SUBJECTS DESIGNS
  • Glenn Gamst, Lawrence S. Meyers, California State University, Sacramento, A. J. Guarino, Auburn University, Alabama
  • Book:
    Analysis of Variance Designs
    Published online:
    05 June 2012
    Print publication:
    01 September 2008, pp 287-324

  • Summary

    COMBINING TWO WITHIN-SUBJECTS FACTORS

    A two-way within-subjects design is one in which the levels of two within-subjects variables are combined factorially. Recall from Chapter 11 that within-subjects independent variables can be either time related or not. There are three possible combinations of types of within-subjects variables that can be represented in a two-way design: (a) both can be time independent, (b) one can be time independent and the other time based, and (c) both can be time based. This latter combination is not often found in the research literature; the other two are more common, and we provide illustrations of them here.

    • Both variables time independent: Students can be asked to indicate if short bursts of music are either rock or classical (music variable) under either silent or noisy conditions (noise variable). In such a situation, participants would be exposed randomly to the various combinations of the independent variables. The dependent variable could be speed of recognition of the music.

    • […]

  • 2 - The term vocabulary and postings lists

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 18-44

  • Summary

    Recall the major steps in inverted index construction:

  • Collect the documents to be indexed.

  • Tokenize the text.

  • Do linguistic preprocessing of tokens.

  • Index the documents that each term occurs in.

    • In this chapter, we first briefly mention how the basic unit of a document can be defined and how the character sequence that it comprises is determined (Section 2.1). We then examine in detail some of the substantive linguistic issues of tokenization and linguistic preprocessing, which determine the vocabulary of terms that a system uses (Section 2.2). Tokenization is the process of chopping character streams into tokens; linguistic preprocessing then deals with building equivalence classes of tokens, which are the set of terms that are indexed. Indexing itself is covered in Chapters 1 and 4. Then we return to the implementation of postings lists. In Section 2.3, we examine an extended postings list data structure that supports faster querying, and Section 2.4 covers building postings data structures suitable for handling phrase and proximity queries, of the sort that commonly appear in both extended Boolean models and on the web.

    Document delineation and character sequence decoding

    Obtaining the character sequence in a document

    Digital documents that are the input to an indexing process are typically bytes in a file or on a web server. The first step of processing is to convert this byte sequence into a linear sequence of characters.

  • 1 - Boolean retrieval

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 1-17

  • Summary

    INFORMATION RETRIEVAL

    The meaning of the term information retrieval (IR) can be very broad. Just getting a credit card out of your wallet so that you can type in the card number is a form of information retrieval. However, as an academic field of study, information retrieval might be defined thus:

    Information retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers).

    As defined in this way, information retrieval used to be an activity that only a few people engaged in: reference librarians, paralegals, and similar professional searchers. Now the world has changed, and hundreds of millions of people engage in information retrieval every day when they use a web search engine or search their email. Information retrieval is fast becoming the dominant form of information access, overtaking traditional database-style searching (the sort that is going on when a clerk says to you: “I'm sorry, I can only look up your order if you can give me your order ID”).

    Information retrieval can also cover other kinds of data and information problems beyond that specified in the core definition above. The term “unstructured data” refers to data that does not have clear, semantically overt, easy-for-a-computer structure. It is the opposite of structured data, the canonical example of which is a relational database, of the sort companies usually use to maintain product inventories and personnel records.

  • 18 - Matrix decompositions and latent semantic indexing

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 369-384

  • Summary

    On page 113, we introduced the notion of a term-document matrix: an M × N matrix C, each of whose rows represents a term and each of whose columns represents a document in the collection. Even for a collection of modest size, the term-document matrix C is likely to have several tens of thousands of rows and columns. In Section 18.1.1, we first develop a class of operations from linear algebra, known as matrix decomposition. In Section 18.2, we use a special form of matrix decomposition to construct a low-rank approximation to the term-document matrix. In Section 18.3 we examine the application of such low-rank approximations to indexing and retrieving documents, a technique referred to as latent semantic indexing. Although latent semantic indexing has not been established as a significant force in scoring and ranking for information retrieval (IR), it remains an intriguing approach to clustering in a number of domains including for collections of text documents (Section 16.6, page 343). Understanding its full potential remains an area of active research.

    Readers who do not require a refresher on linear algebra may skip Section 18.1, although Example 18.1 is especially recommended as it highlights a property of eigenvalues that we exploit later in the chapter.

    Linear algebra review

    We briefly review some necessary background in linear algebra. Let C be an M × N matrix with real-valued entries; for a term–document matrix, all entries are in fact non-negative.

  • 13 - Text classification and Naive Bayes

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 234-265

  • Summary

    Thus far, this book has mainly discussed the process of ad hoc retrieval, where users have transient information needs that they try to address by posing one or more queries to a search engine. However, many users have ongoing information needs. For example, you might need to track developments in multicore computer chips. One way of doing this is to issue the query multicore and computer and chip against an index of recent newswire articles each morning. In this and the following two chapters we examine the question: How can this repetitive task be automated? To this end, many systems support standing queries. A standing query is like any other query except that it is periodically executed on a collection to which new documents are incrementally added over time.

    If your standing query is just multicore and computer and chip, you will tend to miss many relevant new articles which use other terms such as multicore processors. To achieve good recall, standing queries thus have to be refined over time and can gradually become quite complex. In this example, using a Boolean search engine with stemming, you might end up with a query like (multicore or multi-core) and (chip or processor or microprocessor).

    To capture the generality and scope of the problem space to which standing queries belong, we now introduce the general notion of a classification problem. Given a set of classes, we seek to determine which class(es) a given object belongs to.

  • 3 - Dictionaries and tolerant retrieval

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 45-60

  • Summary

    WILDCARD QUERY

    In Chapters 1 and 2, we developed the ideas underlying inverted indexes for handling Boolean and proximity queries. Here, we develop techniques that are robust to typographical errors in the query, as well as alternative spellings. In Section 3.1, we develop data structures that help the search for terms in the vocabulary in an inverted index. In Section 3.2, we study the idea of a wildcard query: a query such as *a*e*i*o*u*, which seeks documents containing any term that includes all the five vowels in sequence. The * symbol indicates any (possibly empty) string of characters. Users pose such queries to a search engine when they are uncertain about how to spell a query term, or seek documents containing variants of a query term; for instance, the query automat* seeks documents containing any of the terms automatic, automation, and automated.

    We then turn to other forms of imprecisely posed queries, focusing on spelling errors in Section 3.3. Users make spelling errors either by accident, or because the term they are searching for (e.g., Herman) has no unambiguous spelling in the collection. We detail a number of techniques for correcting spelling errors in queries, one term at a time as well as for an entire string of query terms. Finally, in Section 3.4 we study a method for seeking vocabulary terms that are phonetically close to the query term(s).

  • 20 - Web crawling and indexes

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 405-420

  • Summary

    Overview

    Web crawling is the process by which we gather pages from the Web to index them and support a search engine. The objective of crawling is to quickly and efficiently gather as many useful web pages as possible, together with the link structure that interconnects them. In Chapter 19, we studied the complexities of the Web stemming from its creation by millions of uncoordinated individuals. In this chapter, we study the resulting difficulties for crawling the Web. The focus of this chapter is the component shown in Figure 19.7 as web crawler; it is sometimes referred to as a spider.

    The goal of this chapter is not to describe how to build the crawler for a full-scale commercial web search engine. We focus instead on a range of issues that are generic to crawling from the student project scale to substantial research projects. We begin (Section 20.1.1) by listing desiderata for web crawlers, and then discuss in Section 20.2 how each of these issues is addressed. The remainder of this chapter describes the architecture and some implementation details for a distributed web crawler that satisfies these features. Section 20.3 discusses distributing indexes across many machines for a web-scale implementation.

    Features a crawler must provide

    We list the desiderata for web crawlers in two categories: features that web crawlers must provide, followed by features they should provide.

  • 6 - Scoring, term weighting, and the vector space model

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 100-123

  • Summary

    Thus far, we have dealt with indexes that support Boolean queries: A document either matches or does not match a query. In the case of large document collections, the resulting number of matching documents can far exceed the number a human user could possibly sift through. Accordingly, it is essential for a search engine to rank-order the documents matching a query. To do this, the search engine computes, for each matching document, a score with respect to the query at hand. In this chapter, we initiate the study of assigning a score to a (query, document) pair. This chapter consists of three main ideas.

  • We introduce parametric and zone indexes in Section 6.1, which serve two purposes. First, they allow us to index and retrieve documents by metadata, such as the language in which a document is written. Second, they give us a simple means for scoring (and thereby ranking) documents in response to a query.

  • Next, in Section 6.2 we develop the idea of weighting the importance of a term in a document, based on the statistics of occurrence of the term.

  • In Section 6.3, we show that by viewing each document as a vector of such weights, we can compute a score between a query and each document. This view is known as vector space scoring.

    • Section 6.4 develops several variants of term-weighting for the vector space model. Chapter 7 develops computational aspects of vector space scoring and related topics.

  • 16 - Flat clustering

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 321-345

  • Summary

    Clustering algorithms group a set of documents into subsets or clusters. The algorithms' goal is to create clusters that are coherent internally, but clearly different from each other. In other words, documents within a cluster should be as similar as possible; and documents in one cluster should be as dissimilar as possible from documents in other clusters.

    Clustering is the most common form of unsupervised learning. No supervision means that there is no human expert who has assigned documents to classes. In clustering, it is the distribution and makeup of the data that will determine cluster membership. A simple example is Figure 16.1. It is visually clear that there are three distinct clusters of points. This chapter and Chapter 17 introduce algorithms that find such clusters in an unsupervised fashion.

    The difference between clustering and classification may not seem great at first. After all, in both cases we have a partition of a set of documents into groups. But as we will see the two problems are fundamentally different. Classification is a form of supervised learning (Chapter 13, page 237): Our goal is to replicate a categorical distinction that a human supervisor imposes on the data. In unsupervised learning, of which clustering is the most important example, we have no such teacher to guide us.

    The key input to a clustering algorithm is the distance measure. In Figure 16.1, the distance measure is distance in the two-dimensional (2D) plane.

  • 15 - Support vector machines and machine learning on documents

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 293-320

  • Summary

    Improving classifier effectiveness has been an area of intensive machine-learning research over the last two decades, and this work has led to a new generation of state-of-the-art classifiers, such as support vector machines, boosted decision trees, regularized logistic regression, neural networks, and random forests. Many of these methods, including support vector machines (SVMs), the main topic of this chapter, have been applied with success to information retrieval problems, particularly text classification. An SVM is a kind of large-margin classifier: It is a vector-space–based machine-learning method where the goal is to find a decision boundary between two classes that is maximally far from any point in the training data (possibly discounting some points as outliers or noise).

    We will initially motivate and develop SVMs for the case of two-class data sets that are separable by a linear classifier (Section 15.1), and then extend the model in Section 15.2 to nonseparable data, multiclass problems, and nonlinear models, and also present some additional discussion of SVM performance. The chapter then moves to consider the practical deployment of text classifiers in Section 15.3: What sorts of classifiers are appropriate when, and how can you exploit domain-specific text features in classification? Finally, we will consider how the machine-learning technology that we have been building for text classification can be applied back to the problem of learning how to rank documents in ad hoc retrieval (Section 15.4).

  • 10 - XML retrieval

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 178-200

  • Summary

    Information retrieval (IR) systems are often contrasted with relational databases. Traditionally, IR systems have retrieved information from unstructured text – by which we mean “raw” text without markup. Databases are designed for querying relational data, sets of records that have values for predefined attributes such as employee number, title, and salary. There are fundamental differences between IR and database systems in terms of retrieval model, data structures, and query language as shown in Table 10.1.

    Some highly structured text search problems are most efficiently handled by a relational database; for example, if the employee table contains an attribute for short textual job descriptions and you want to find all employees who are involved with invoicing. In this case, the SQL query:

    select lastname from employees where job_desc like ‘invoic%’;

    may be sufficient to satisfy your information need with high precision and recall.

    STRUCTURED RETRIEVAL

    However, many structured data sources containing text are best modeled as structured documents rather than relational data. We call the search over such structured documents structured retrieval. Queries in structured retrieval can be either structured or unstructured, but we assume in this chapter that the collection consists only of structured documents. Applications of structured retrieval include digital libraries, patent databases, blogs, text in which entities like persons and locations have been tagged (in a process called named entity tagging), and output from office suites like OpenOffice that save documents as marked up text.

  • Preface

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp xv-xxii

  • Summary

    As recently as the 1990s, studies showed that most people preferred getting information from other people rather than from information retrieval (IR) systems. Of course, in that time period, most people also used human travel agents to book their travel. However, during the last decade, relentless optimization of information retrieval effectiveness has driven web search engines to new quality levels at which most people are satisfied most of the time, and web search has become a standard and often preferred source of information finding. For example, the 2004 Pew Internet Survey (Fallows 2004) found that “92% of Internet users say the Internet is a good place to go for getting everyday information.” To the surprise of many, the field of information retrieval has moved from being a primarily academic discipline to being the basis underlying most people's preferred means of information access. This book presents the scientific underpinnings of this field, at a level accessible to graduate students as well as advanced undergraduates.

    Information retrieval did not begin with the Web. In response to various challenges of providing information access, the field of IR evolved to give principled approaches to searching various forms of content. The field began with scientific publications and library records but soon spread to other forms of content, particularly those of information professionals, such as journalists, lawyers, and doctors.

  • 17 - Hierarchical clustering

  • Christopher D. Manning, Stanford University, California, Prabhakar Raghavan, Hinrich Schütze, Universität Stuttgart
  • Book:
    Introduction to Information Retrieval
    Published online:
    05 June 2012
    Print publication:
    07 July 2008, pp 346-368

  • Summary

    Flat clustering is efficient and conceptually simple, but as we saw in Chapter 16 it has a number of drawbacks. The algorithms introduced in Chapter 16 return a flat unstructured set of clusters, require a prespecified number of clusters as input and are nondeterministic. Hierarchical clustering (or hierarchic clustering) outputs a hierarchy, a structure that is more informative than the unstructured set of clusters returned by flat clustering. Hierarchical clustering does not require us to prespecify the number of clusters and most hierarchical algorithms that have been used in information retrieval (IR) are deterministic. These advantages of hierarchical clustering come at the cost of lower efficiency. The most common hierarchical clustering algorithms have a complexity that is at least quadratic in the number of documents compared to the linear complexity of K-means and EM (cf. Section 16.4, page 335).

    This chapter first introduces agglomerative hierarchical clustering (Section 17.1) and presents four different agglomerative algorithms, in Sections 17.2 through 17.4, which differ in the similarity measures they employ: single-link, complete-link, group-average, and centroid similarity. We then discuss the optimality conditions of hierarchical clustering in Section 17.5. Section 17.6 introduces top-down (or divisive) hierarchical clustering. Section 17.7 looks at labeling clusters automatically, a problem that must be solved whenever humans interact with the output of clustering. We discuss implementation issues in Section 17.8. Section 17.9 provides pointers to further reading, including references to soft hierarchical clustering, which we do not cover in this book.

Page 1708 of 1900