Search (27 results, page 1 of 2)

0.037393916 = product of:
  0.112181745 = sum of:
    0.04816959 = weight(_text_:wide in 4972) [ClassicSimilarity], result of:
      0.04816959 = score(doc=4972,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.24476713 = fieldWeight in 4972, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4972)
    0.064012155 = weight(_text_:web in 4972) [ClassicSimilarity], result of:
      0.064012155 = score(doc=4972,freq=12.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.4416067 = fieldWeight in 4972, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4972)
  0.33333334 = coord(2/6)

Abstract

Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.

0.025285622 = product of:
  0.075856864 = sum of:
    0.057803504 = weight(_text_:wide in 5589) [ClassicSimilarity], result of:
      0.057803504 = score(doc=5589,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.29372054 = fieldWeight in 5589, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.046875 = fieldNorm(doc=5589)
    0.01805336 = product of:
      0.03610672 = sum of:
        0.03610672 = weight(_text_:22 in 5589) [ClassicSimilarity], result of:
          0.03610672 = score(doc=5589,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.23214069 = fieldWeight in 5589, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=5589)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

Content analysis is a highly flexible research method that has been widely used in library and information science (LIS) studies with varying research goals and objectives. The research method is applied in qualitative, quantitative, and sometimes mixed modes of research frameworks and employs a wide range of analytical techniques to generate findings and put them into context. This article characterizes content analysis as a systematic, rigorous approach to analyzing documents obtained or generated in the course of research. It briefly describes the steps involved in content analysis, differentiates between quantitative and qualitative content analysis, and shows that content analysis serves the purposes of both quantitative research and qualitative research. The authors draw on selected LIS studies that have used content analysis to illustrate the concepts addressed in the article. The article also serves as a gateway to methodological books and articles that provide more detail about aspects of content analysis discussed only briefly in the article.

Source

Library trends. 55(2006) no.1, S.22-45

0.017477274 = product of:
  0.10486364 = sum of:
    0.10486364 = weight(_text_:computer in 7514) [ClassicSimilarity], result of:
      0.10486364 = score(doc=7514,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.6460321 = fieldWeight in 7514, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.125 = fieldNorm(doc=7514)
  0.16666667 = coord(1/6)

Source

Encyclopedia of computer science and technology, vol.6

0.017477274 = product of:
  0.10486364 = sum of:
    0.10486364 = weight(_text_:computer in 6830) [ClassicSimilarity], result of:
      0.10486364 = score(doc=6830,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.6460321 = fieldWeight in 6830, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.125 = fieldNorm(doc=6830)
  0.16666667 = coord(1/6)

0.015447874 = product of:
  0.09268724 = sum of:
    0.09268724 = weight(_text_:computer in 4236) [ClassicSimilarity], result of:
      0.09268724 = score(doc=4236,freq=4.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.5710171 = fieldWeight in 4236, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.078125 = fieldNorm(doc=4236)
  0.16666667 = coord(1/6)

Source

Social science computer review. 11(1993) no.3, S.283-291

0.015292614 = product of:
  0.09175568 = sum of:
    0.09175568 = weight(_text_:computer in 5270) [ClassicSimilarity], result of:
      0.09175568 = score(doc=5270,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.56527805 = fieldWeight in 5270, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.109375 = fieldNorm(doc=5270)
  0.16666667 = coord(1/6)

Source

Text analysis and computer. Ed.: C. Züll et al

0.013107955 = product of:
  0.07864773 = sum of:
    0.07864773 = weight(_text_:computer in 1044) [ClassicSimilarity], result of:
      0.07864773 = score(doc=1044,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.48452407 = fieldWeight in 1044, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.09375 = fieldNorm(doc=1044)
  0.16666667 = coord(1/6)

0.012212615 = product of:
  0.07327569 = sum of:
    0.07327569 = weight(_text_:computer in 5392) [ClassicSimilarity], result of:
      0.07327569 = score(doc=5392,freq=10.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.45142862 = fieldWeight in 5392, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5392)
  0.16666667 = coord(1/6)

Content

SCHMIDT, K.M.: Concepts - content - meaning: an introduction; DUCHASTEL, J. et al.: The SACAO project: using computation toward textual data analysis; PAQUIN, L.-C. u. L. DUPUY: An approach to expertise transfer: computer-assisted text analysis; HOGENRAAD, R., Y. BESTGEN u. J.-L. NYSTEN: Terrorist rhetoric: texture and architecture; MOHLER, P.P.: On the interaction between reading and computing: an interpretative approach to content analysis; LANCASHIRE, I.: Computer tools for cognitive stylistics; MERGENTHALER, E.: An outline of knowledge based text analysis; NAMENWIRTH, J.Z.: Ideography in computer-aided content analysis; WEBER, R.P. u. J.Z. Namenwirth: Content-analytic indicators: a self-critique; McKINNON, A.: Optimizing the aberrant frequency word technique; ROSATI, R.: Factor analysis in classical archaeology: export patterns of Attic pottery trade; PETRILLO, P.S.: Old and new worlds: ancient coinage and modern technology; DARANYI, S., S. MARJAI u.a.: Caryatids and the measurement of semiosis in architecture; ZARRI, G.P.: Intelligent information retrieval: an application in the field of historical biographical data; BOUCHARD, G., R. ROY u.a.: Computers and genealogy: from family reconstitution to population reconstruction; DEMÉLAS-BOHY, M.-D. u. M. RENAUD: Instability, networks and political parties: a political history expert system prototype; DARANYI, S., A. ABRANYI u. G. KOVACS: Knowledge extraction from ethnopoetic texts by multivariate statistical methods; FRAUTSCHI, R.L.: Measures of narrative voice in French prose fiction applied to textual samples from the enlightenment to the twentieth century; DANNENBERG, R. u.a.: A project in computer music: the musician's workbench

0.011523506 = product of:
  0.06914103 = sum of:
    0.06914103 = weight(_text_:web in 1863) [ClassicSimilarity], result of:
      0.06914103 = score(doc=1863,freq=14.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.47698978 = fieldWeight in 1863, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1863)
  0.16666667 = coord(1/6)

Abstract

This research explores the use of genre as a document descriptor in order to improve the effectiveness of Web searching. A major issue to be resolved is the identification of what document categories should be used as genres. As genre is a kind of folk typology, document categories must enjoy widespread recognition by their intended user groups in order to qualify as genres. Three user studies were conducted to develop a genre palette and show that it is recognizable to users. (Palette is a term used to denote a classification, attributable to Karlgren, Bretan, Dewe, Hallberg, and Wolkert, 1998.) To simplify the users' classification task, it was decided to focus on Web pages from the edu domain. The first study was a survey of user terminology for Web pages. Three participants separated 100 Web page printouts into stacks according to genre, assigning names and definitions to each genre. The second study aimed to refine the resulting set of 48 (often conceptually and lexically similar) genre names and definitions into a smaller palette of user-preferred terminology. Ten participants classified the same 100 Web pages. A set of five principles for creating a genre palette from individuals' sortings was developed, and the list of 48 was trimmed to 18 genres. The third study aimed to show that users would agree on the genres of Web pages when choosing from the genre palette. In an online experiment in which 257 participants categorized a new set of 55 pages using the 18 genres, on average, over 70% agreed on the genre of each page. Suggestions for improving the genre palette and future directions for the work are discussed.

0.010813512 = product of:
  0.06488107 = sum of:
    0.06488107 = weight(_text_:computer in 5828) [ClassicSimilarity], result of:
      0.06488107 = score(doc=5828,freq=4.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.39971197 = fieldWeight in 5828, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0546875 = fieldNorm(doc=5828)
  0.16666667 = coord(1/6)

Abstract

Issues concerning the formulation and application of a model of how humans value information are examined. Formulation of a value function is based on research from modelling, value assessment, human information seeking behavior, and human decision making. The proposed function is incorporated into a computer-based fiction retrieval system and evaluated using data from nine searches. Evaluation is based on the ability of an individual's value function to discriminate among novels selected, rejected, and not considered. The results are discussed in terms of both formulation and utilization of a value function as well as the implications for extending the proposed formulation to other information seeking environments

0.008028265 = product of:
  0.04816959 = sum of:
    0.04816959 = weight(_text_:wide in 2667) [ClassicSimilarity], result of:
      0.04816959 = score(doc=2667,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.24476713 = fieldWeight in 2667, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2667)
  0.16666667 = coord(1/6)

Abstract

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

0.0075439056 = product of:
  0.045263432 = sum of:
    0.045263432 = weight(_text_:web in 2669) [ClassicSimilarity], result of:
      0.045263432 = score(doc=2669,freq=6.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.3122631 = fieldWeight in 2669, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2669)
  0.16666667 = coord(1/6)

Abstract

In this paper, we focus on applying sentiment analysis to resources from online art collections, by exploiting, as information source, tags intended as textual traces that visitors leave to comment artworks on social platforms. We present a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions. The ontology is inspired by the Plutchik's circumplex model, a well-founded psychological model of human emotions. Users can be involved in the creation of the emotional space, through a graphical interactive interface. The development of such semantic space enables new ways of accessing and exploring art collections. The affective categorization model and the emotion detection output are encoded into W3C ontology languages. This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks, and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community. The proposal has been evaluated against a real-word case study, a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection, and validated through a user study.

0.006871327 = product of:
  0.04122796 = sum of:
    0.04122796 = product of:
      0.08245592 = sum of:
        0.08245592 = weight(_text_:programs in 5640) [ClassicSimilarity], result of:
          0.08245592 = score(doc=5640,freq=2.0), product of:
            0.25748047 = queryWeight, product of:
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.044416238 = queryNorm
            0.32024145 = fieldWeight in 5640, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5640)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Abstract

Aboutness ranks amongst our field's greatest bugbears. What is a work about? How can this be known? This mirrors debates within the philosophy of language, where the concept of representation has similarly evaded satisfactory definition. This paper proposes that we abandon the strong sense of the word aboutness, which seems to promise some inherent relationship between work and subject, or, in philosophical terms, between word and world. Instead, we seek an etymological reset to the older sense of aboutness as "in the vicinity, nearby; in some place or various places nearby; all over a surface." To distinguish this sense in the context of information studies, we introduce the term episemantics. The authors have each independently applied this term in slightly different contexts and scales (Hauser 2018a; Tennis 2016), and this article presents a unified definition of the term and guidelines for applying it at the scale of both words and works. The resulting weak concept of aboutness is pragmatic, in Star's sense of a focus on consequences over antecedents, while reserving space for the critique and improvement of aboutness determinations within various contexts and research programs. The paper finishes with a discussion of the implication of the concept of episemantics and methodological possibilities it offers for knowledge organization research and practice. We draw inspiration from Melvil Dewey's use of physical aroundness in his first classification system and ask how aroundness might be more effectively operationalized in digital environments.

0.005461648 = product of:
  0.03276989 = sum of:
    0.03276989 = weight(_text_:computer in 3618) [ClassicSimilarity], result of:
      0.03276989 = score(doc=3618,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.20188503 = fieldWeight in 3618, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3618)
  0.16666667 = coord(1/6)

Abstract

Purpose - The purpose of this paper is to examine the art historical antecedents of providing subject access to images. After reviewing the assumptions and limitations inherent in the most prevalent descriptive method, the paper seeks to introduce a new model that allows for more comprehensive representation of visually-based cultural materials. Design/methodology/approach - The paper presents a literature-based conceptual analysis, taking Panofsky's theory of iconography and iconology as the starting-point. Panofsky's conceptual model, while appropriate for art created in the Western academic tradition, ignores or misrepresents work from other eras or cultures. Continued dependence on Panofskian descriptive methods limits the functionality and usefulness of image representation systems. Findings - The paper recommends the development of a more precise and inclusive descriptive model for art objects, which is based on the premise that art is not another sort of text, and should not be interpreted as such. Practical implications - The paper provides suggestions for the development of representation models that will enhance the description of non-textual artifacts. Originality/value - The paper addresses issues in information science, the history of art, and computer science, and suggests that a new descriptive model would be of great value to both humanist and social science scholars.

0.0052265706 = product of:
  0.031359423 = sum of:
    0.031359423 = weight(_text_:web in 4444) [ClassicSimilarity], result of:
      0.031359423 = score(doc=4444,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.21634221 = fieldWeight in 4444, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=4444)
  0.16666667 = coord(1/6)

Abstract

The paper establishes a taxonomy of image-text relationships that reflects the ways that images and text interact. It is applicable to all subject areas and document types. The taxonomy was developed to answer the research question: how does an illustration relate to the text with which it is associated, or, what are the functions of illustration? Developed in a two-stage process - first, analysis of relevant research in children's literature, dictionary development, education, journalism, and library and information design and, second, subsequent application of the first version of the taxonomy to 954 image-text pairs in 45 Web pages (pages with educational content for children, online newspapers, and retail business pages) - the taxonomy identifies 49 relationships and groups them in three categories according to the closeness of the conceptual relationship between image and text. The paper uses qualitative content analysis to illustrate use of the taxonomy to analyze four image-text pairs in government publications and discusses the implications of the research for information retrieval and document design.

0.0052265706 = product of:
  0.031359423 = sum of:
    0.031359423 = weight(_text_:web in 4570) [ClassicSimilarity], result of:
      0.031359423 = score(doc=4570,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.21634221 = fieldWeight in 4570, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=4570)
  0.16666667 = coord(1/6)

Abstract

Some collections cover many topics, while others are narrowly focused an a limited number of topics. We introduce the concept of the "scope" of a collection of documents and we compare two ways of measuring lt. These measures are based an the distances between documents. The first uses the overlap of words between pairs of documents. The second measure uses a novel method that calculates the semantic relatedness to pairs of words from the documents. Those values are combined to obtain an overall distance between the documents. The main validation for the measures compared Web pages categorized by Yahoo. Sets of pages sampied from broad categories were determined to have a higher scope than sets derived from subcategories. The measure was significant and confirmed the expected difference in scope. Finally, we discuss other measures related to scope.

0.005014823 = product of:
  0.030088935 = sum of:
    0.030088935 = product of:
      0.06017787 = sum of:
        0.06017787 = weight(_text_:22 in 5835) [ClassicSimilarity], result of:
          0.06017787 = score(doc=5835,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.38690117 = fieldWeight in 5835, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.078125 = fieldNorm(doc=5835)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Date

5. 8.2006 13:22:44

0.0048169587 = product of:
  0.028901752 = sum of:
    0.028901752 = weight(_text_:wide in 3651) [ClassicSimilarity], result of:
      0.028901752 = score(doc=3651,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.14686027 = fieldWeight in 3651, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0234375 = fieldNorm(doc=3651)
  0.16666667 = coord(1/6)

Abstract

This paper, presented at the Ottawa Conference an the Conceptual Basis of the Classification of Knowledge, in 1971, is one of Fairthorne's more perceptive works and deserves a wide audience, especially as it breaks new ground in classification theory. In discussing the notion of discourse, he makes a "distinction between what discourse mentions and what discourse is about" [emphasis added], considered as a "fundamental factor to the relativistic nature of bibliographic classification" (p. 360). A table of mathematical functions, for example, describes exactly something represented by a collection of digits, but, without a preface, this table does not fit into a broader context. Some indication of the author's intent ls needed to fit the table into a broader context. This intent may appear in a title, chapter heading, class number or some other aid. Discourse an and discourse about something "cannot be determined solely from what it mentions" (p. 361). Some kind of background is needed. Fairthorne further develops the theme that knowledge about a subject comes from previous knowledge, thus adding a temporal factor to classification. "Some extra textual criteria are needed" in order to classify (p. 362). For example, "documents that mention the same things, but are an different topics, will have different ancestors, in the sense of preceding documents to which they are linked by various bibliographic characteristics ... [and] ... they will have different descendants" (p. 363). The classifier has to distinguish between documents that "mention exactly the same thing" but are not about the same thing. The classifier does this by classifying "sets of documents that form their histories, their bibliographic world lines" (p. 363). The practice of citation is one method of performing the linking and presents a "fan" of documents connected by a chain of citations to past work. The fan is seen as the effect of generations of documents - each generation connected to the previous one, and all ancestral to the present document. Thus, there are levels in temporal structure-that is, antecedent and successor documents-and these require that documents be identified in relation to other documents. This gives a set of documents an "irrevocable order," a loose order which Fairthorne calls "bibliographic time," and which is "generated by the fact of continual growth" (p. 364). He does not consider "bibliographic time" to be an equivalent to physical time because bibliographic events, as part of communication, require delay. Sets of documents, as indicated above, rather than single works, are used in classification. While an event, a person, a unique feature of the environment, may create a class of one-such as the French Revolution, Napoleon, Niagara Falls-revolutions, emperors, and waterfalls are sets which, as sets, will subsume individuals and make normal classes.

0.0040118583 = product of:
  0.024071148 = sum of:
    0.024071148 = product of:
      0.048142295 = sum of:
        0.048142295 = weight(_text_:22 in 5830) [ClassicSimilarity], result of:
          0.048142295 = score(doc=5830,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.30952093 = fieldWeight in 5830, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=5830)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Date

5. 8.2006 13:22:08

0.0040118583 = product of:
  0.024071148 = sum of:
    0.024071148 = product of:
      0.048142295 = sum of:
        0.048142295 = weight(_text_:22 in 251) [ClassicSimilarity], result of:
          0.048142295 = score(doc=251,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.30952093 = fieldWeight in 251, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=251)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Date

22. 5.2021 12:43:05