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37983 results in Cambridge Textbooks

Page 1709 of 1900

  • 5 - Index compression

  • 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 78-99

  • Summary

    Chapter 1 introduced the dictionary and the inverted index as the central data structures in information retrieval (IR). In this chapter, we employ a number of compression techniques for dictionary and inverted index that are essential for efficient IR systems.

    One benefit of compression is immediately clear. We need less disk space. As we will see, compression ratios of 1:4 are easy to achieve, potentially cutting the cost of storing the index by 75%.

    There are two more subtle benefits of compression. The first is increased use of caching. Search systems use some parts of the dictionary and the index much more than others. For example, if we cache the postings list of a frequently used query term t, then the computations necessary for responding to the one-term query t can be entirely done in memory. With compression, we can fit a lot more information into main memory. Instead of having to expend a disk seek when processing a query with t, we instead access its postings list in memory and decompress it. As we will see below, there are simple and efficient decompression methods, so that the penalty of having to decompress the postings list is small. As a result, we are able to decrease the response time of the IR system substantially. Because memory is a more expensive resource than disk space, increased speed owing to caching – rather than decreased space requirements – is often the prime motivator for compression.

  • 4 - Index construction

  • 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 61-77

  • Summary

    INDEXING INDEXER

    In this chapter, we look at how to construct an inverted index. We call this process index construction or indexing; the process or machine that performs it the indexer. The design of indexing algorithms is governed by hardware constraints. We therefore begin this chapter with a review of the basics of computer hardware that are relevant for indexing. We then introduce blocked sort-based indexing (Section 4.2), an efficient single-machine algorithm designed for static collections that can be viewed as a more scalable version of the basic sort-based indexing algorithm we introduced in Chapter 1. Section 4.3 describes single-pass in-memory indexing, an algorithm that has even better scaling properties because it does not hold the vocabulary in memory. For very large collections like the web, indexing has to be distributed over computer clusters with hundreds or thousands of machines. We discuss this in Section 4.4. Collections with frequent changes require dynamic indexing introduced in Section 4.5 so that changes in the collection are immediately reflected in the index. Finally, we cover some complicating issues that can arise in indexing – such as security and indexes for ranked retrieval – in Section 4.6.

    Index construction interacts with several topics covered in other chapters. The indexer needs raw text, but documents are encoded in many ways (see Chapter 2). Indexers compress and decompress intermediate files and the final index (see Chapter 5).

  • 9 - Relevance feedback and query expansion

  • 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 162-177

  • Summary

    SYNONYMY

    In most collections, the same concept may be referred to using different words. This issue, known as synonymy, has an impact on the recall of most information retrieval (IR) systems. For example, you would want a search for aircraft to match plane (but only for references to an airplane, not a woodworking plane), and for a search on thermodynamics to match references to heat in appropriate discussions. Users often attempt to address this problem themselves by manually refining a query, as was discussed in Section 1.4; in this chapter, we discuss ways in which a system can help with query refinement, either fully automatically or with the user in the loop.

    The methods for tackling this problem split into two major classes: global methods and local methods. Global methods are techniques for expanding or reformulating query terms independent of the query and results returned from it, so that changes in the query wording will cause the new query to match other semantically similar terms. Global methods include:

  • Query expansion/reformulation with a thesaurus or WordNet (Section 9.2.2)

  • Query expansion via automatic thesaurus generation (Section 9.2.3)

  • Techniques like spelling correction (discussed in Chapter 3)

    • Local methods adjust a query relative to the documents that initially appear to match the query. The basic methods here are:

  • Relevance feedback (Section 9.1)

  • Pseudorelevance feedback, also known as blind relevance feedback (Section 9.1.6)

  • (Global) Indirect relevance feedback (Section 9.1.7)

  • 21 - Link analysis

  • 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 421-440

  • Summary

    The analysis of hyperlinks and the graph structure of the Web has been instrumental in the development of web search. In this chapter, we focus on the use of hyperlinks for ranking web search results. Such link analysis is one of many factors considered by web search engines in computing a composite score for a web page on any given query. We begin by reviewing some basics of the Web as a graph in Section 21.1, then proceed to the technical development of the elements of link analysis for ranking.

    Link analysis for web search has intellectual antecedents in the field of citation analysis, aspects of which overlap with an area known as bibliometrics. These disciplines seek to quantify the influence of scholarly articles by analyzing the pattern of citations among them. Much as citations represent the conferral of authority from a scholarly article to others, link analysis on the Web treats hyperlinks from a web page to another as a conferral of authority. Clearly, not every citation or hyperlink implies such authority conferral; for this reason, simply measuring the quality of a web page by the number of in-links (citations from other pages) is not robust enough. For instance, one may contrive to set up multiple web pages pointing to a target web page, with the intent of artificially boosting the latter's tally of in-links. This phenomenon is referred to as link spam.

  • 8 - Evaluation in information 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 139-161

  • Summary

    We have seen in the preceding chapters many alternatives in designing an information retrieval (IR) system. How do we know which of these techniques are effective in which applications? Should we use stop lists? Should we stem? Should we use inverse document frequency weighting? IR has developed as a highly empirical discipline, requiring careful and thorough evaluation to demonstrate the superior performance of novel techniques on representative document collections.

    In this chapter, we begin with a discussion of measuring the effectiveness of IR systems (Section 8.1) and the test collections that are most often used for this purpose (Section 8.2). We then present the straightforward notion of relevant and nonrelevant documents and the formal evaluation methodology that has been developed for evaluating unranked retrieval results (Section 8.3). This includes explaining the kinds of evaluation measures that are standardly used for document retrieval and related tasks like text classification and why they are appropriate. We then extend these notions and develop further measures for evaluating ranked retrieval results (Section 8.4) and discuss developing reliable and informative test collections (Section 8.5).

    We then step back to introduce the notion of user utility, and how it is approximated by the use of document relevance (Section 8.6). The key utility measure is user happiness. Speed of response and the size of the index are factors in user happiness.

  • 12 - Language models for information 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 218-233

  • Summary

    A common suggestion to users for coming up with good queries is to think of words that would likely appear in a relevant document, and to use those words as the query. The language modeling approach to information retrieval (IR) directly models that idea: A document is a good match to a query if the document model is likely to generate the query, which will in turn happen if the document contains the query words often. This approach thus provides a different realization of some of the basic ideas for document ranking which we saw in Section 6.2 (page 107). Instead of overtly modeling the probability P(R = 1|q, d) of relevance of a document d to a query q, as in the traditional probabilistic approach to IR (Chapter 11), the basic language modeling approach instead builds a probabilistic language model Md from each document d, and ranks documents based on the probability of the model generating the query: P(q|Md).

    In this chapter, we first introduce the concept of language models (Section 12.1) and then describe the basic and most commonly used language modeling approach to IR, the query likelihood model (Section 12.2). After some comparisons between the language modeling approach and other approaches to IR (Section 12.3), we finish by briefly describing various extensions to the language modeling approach (Section 12.4).

    Language models

    Finite automata and language models

    What do we mean by a document model generating a query?

  • 11 - Probabilistic information 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 201-217

  • Summary

    During the discussion of relevance feedback in Section 9.1.2, we observed that if we have some known relevant and nonrelevant documents, then we can straightforwardly start to estimate the probability of a term t appearing in a relevant document P(t|R = 1), and that this could be the basis of a classifier that decides whether documents are relevant or not. In this chapter, we more systematically introduce this probabilistic approach to information retrieval (IR), which provides a different formal basis for a retrieval model and results in different techniques for setting term weights.

    Users start with information needs, which they translate into query representations. Similarly, there are documents, which are converted into document representations (the latter differing at least by how text is tokenized, but perhaps containing fundamentally less information, as when a nonpositional index is used). Based on these two representations, a system tries to determine how well documents satisfy information needs. In the Boolean or vector space models of IR, matching is done in a formally defined but semantically imprecise calculus of index terms. Given only a query, an IR system has an uncertain understanding of the information need. Given the query and document representations, a system has an uncertain guess of whether a document has content relevant to the information need. Probability theory provides a principled foundation for such reasoning under uncertainty.

  • Introduction

  • Translated by M. C. Howatson, St Anne's College, Oxford
  • Edited by Frisbee C. C. Sheffield, King's College London
  • Book:
    Plato: <I>The Symposium</I>
    Published online:
    05 June 2012
    Print publication:
    24 April 2008, pp vii-xxviii

  • Summary

    Plato's writings are typically in the form of dialogues in which Socrates (born 469 BC) discusses philosophical questions with other characters of his day. Most of these are based on known historical figures, but the dialogues are not factual accounts; they are fictional, and often richly dramatic, products of Plato's philosophical imagination. The Symposium is a particularly dramatic work. It is set at the house of Agathon, a tragic poet celebrating his recent victory in 416 BC at one of the great dramatic festivals. Those present are amongst the intellectual elite of the day. They include an exponent of heroic poetry (Phaedrus), an expert in the laws of various Greek states (Pausanias), a representative of medical expertise (Eryximachus), a comic poet (Aristophanes) and a philosopher (Socrates). The guests participate in a symposium, a drinking party for aristocratic circles, on this occasion designed to honour Agathon's victory. Each guest delivers a speech in praise of eros, ‘passionate love’, or ‘desire’. The final speech is delivered by Alcibiades, a notorious associate of Socrates, who talks openly about his love for Socrates, in particular.

  • The Symposium (‘The Drinking Party’)

  • Translated by M. C. Howatson, St Anne's College, Oxford
  • Edited by Frisbee C. C. Sheffield, King's College London
  • Book:
    Plato: <I>The Symposium</I>
    Published online:
    05 June 2012
    Print publication:
    24 April 2008, pp 1-63

  • Summary

    apollodorus: I believe I am quite well prepared to relate the events you are asking me about, for just the other day I happened to be going into Athens from my home in Phalerum when an acquaintance of mine caught sight of me from behind and called after me, jokily,

    ‘Phalerian! You there, Apollodorus! Wait for me, will you?’

    So I stopped and waited.

    ‘I have just been looking for you, Apollodorus. I wanted to get from you the story about that party of Agathon's with Socrates, Alcibiades and the rest, the time when they were all together at dinner, and to hear what they said in their speeches on the subject of love. Someone else was telling me, who had heard about it from Phoenix, son of Philippus, and he said that you knew about it too. Actually he could not give any clear account of it, so you must tell me. You are in the best position to report the words of your friend. But tell me this first’, he went on. ‘Were you at that party yourself or not?’

    ‘It certainly looks as if your informant was rather confused’, I replied, ‘if you think the party you are asking about occurred recently enough for me to be there’.

Page 1709 of 1900