Search (28 results, page 1 of 2)

0.034606628 = product of:
  0.10381988 = sum of:
    0.050336715 = weight(_text_:applications in 932) [ClassicSimilarity], result of:
      0.050336715 = score(doc=932,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.2918479 = fieldWeight in 932, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.046875 = fieldNorm(doc=932)
    0.010999769 = weight(_text_:of in 932) [ClassicSimilarity], result of:
      0.010999769 = score(doc=932,freq=6.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.17955035 = fieldWeight in 932, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=932)
    0.042483397 = weight(_text_:systems in 932) [ClassicSimilarity], result of:
      0.042483397 = score(doc=932,freq=6.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.35286134 = fieldWeight in 932, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=932)
  0.33333334 = coord(3/9)

Abstract

This paper reviews research on automatic summarising in the last decade. This work has grown, stimulated by technology and by evaluation programmes. The paper uses several frameworks to organise the review, for summarising itself, for the factors affecting summarising, for systems, and for evaluation. The review examines the evaluation strategies applied to summarising, the issues they raise, and the major programmes. It considers the input, purpose and output factors investigated in recent summarising research, and discusses the classes of strategy, extractive and non-extractive, that have been explored, illustrating the range of systems built. The conclusions drawn are that automatic summarisation has made valuable progress, with useful applications, better evaluation, and more task understanding. But summarising systems are still poorly motivated in relation to the factors affecting them, and evaluation needs taking much further to engage with the purposes summaries are intended to serve and the contexts in which they are used.

0.019893078 = product of:
  0.08951885 = sum of:
    0.06711562 = weight(_text_:applications in 71) [ClassicSimilarity], result of:
      0.06711562 = score(doc=71,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.38913056 = fieldWeight in 71, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0625 = fieldNorm(doc=71)
    0.022403233 = weight(_text_:of in 71) [ClassicSimilarity], result of:
      0.022403233 = score(doc=71,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.36569026 = fieldWeight in 71, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=71)
  0.22222222 = coord(2/9)

Abstract

Examines statistical techniques for exploiting relevance information to weight search terms. These techniques are presented as a natural extension of weighting methods using information about the distribution of index terms in documents in general. A series of relevance weighting functions is derived and is justified by theoretical considerations. In particular, it is shown that specific weighted search methods are implied by a general probabilistic theory of retrieval. Different applications of relevance weighting are illustrated by experimental results for test collections

Source

Journal of the American Society for Information Science. 27(1976), S.129-146

0.019523773 = product of:
  0.08785698 = sum of:
    0.06711562 = weight(_text_:applications in 4811) [ClassicSimilarity], result of:
      0.06711562 = score(doc=4811,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.38913056 = fieldWeight in 4811, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0625 = fieldNorm(doc=4811)
    0.020741362 = weight(_text_:of in 4811) [ClassicSimilarity], result of:
      0.020741362 = score(doc=4811,freq=12.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.33856338 = fieldWeight in 4811, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=4811)
  0.22222222 = coord(2/9)

Abstract

Presents a view of the scope of artificial intelligence (AI) in information retrieval (IR). Considers potential roles of AI and IR, evaluating AI from a realistic point od view and within a wide information management potential, not just because AI is itself insufficiently developed, but because many information management tasks are properly shallow information processing ones. There is nevertheless an important place for specific applications of AI or AI-derived technology when particular constraints can be placed on the information management tasks involved

Source

Journal of the American Society for Information Science. 42(1991) no.8, S.558-565

0.015378739 = product of:
  0.06920432 = sum of:
    0.05872617 = weight(_text_:applications in 4328) [ClassicSimilarity], result of:
      0.05872617 = score(doc=4328,freq=2.0), product of:
        0.17247584 = queryWeight, product of:
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.03917671 = queryNorm
        0.34048924 = fieldWeight in 4328, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4025097 = idf(docFreq=1471, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4328)
    0.010478153 = weight(_text_:of in 4328) [ClassicSimilarity], result of:
      0.010478153 = score(doc=4328,freq=4.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.17103596 = fieldWeight in 4328, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4328)
  0.22222222 = coord(2/9)

Abstract

Purpose - This short note seeks to respond to Hjørland and Pederson's paper "A substantive theory of classification for information retrieval" which starts from Sparck Jones's, "Some thoughts on classification for retrieval", originally published in 1970. Design/methodology/approach - The note comments on the context in which the 1970 paper was written, and on Hjørland and Pedersen's views, emphasising the need for well-grounded classification theory and application. Findings - The note maintains that text-based, a posteriori, classification, as increasingly found in applications, is likely to be more useful, in general, than a priori classification. Originality/value - The note elaborates on points made in a well-received earlier paper.

Source

Journal of documentation. 61(2005) no.5, S.598-601

0.014635116 = product of:
  0.06585802 = sum of:
    0.016802425 = weight(_text_:of in 2934) [ClassicSimilarity], result of:
      0.016802425 = score(doc=2934,freq=14.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2742677 = fieldWeight in 2934, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=2934)
    0.0490556 = weight(_text_:systems in 2934) [ClassicSimilarity], result of:
      0.0490556 = score(doc=2934,freq=8.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.4074492 = fieldWeight in 2934, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.046875 = fieldNorm(doc=2934)
  0.22222222 = coord(2/9)

Abstract

This comprehensive state-of-the-art book is the first devoted to the important and timely issue of evaluating NLP systems. It addresses the whole area of NLP system evaluation, including aims and scope, problems and methodology. The authors provide a wide-ranging and careful analysis of evaluation concepts, reinforced with extensive illustrations; they relate systems to their environments and develop a framework for proper evaluation. The discussion of principles is completed by a detailed review of practice and strategies in the field, covering both systems for specific tasks, like translation, and core language processors. The methodology lessons drawn from the analysis and review are applied in a series of example cases. A comprehensive bibliography, a subject index, and term glossary are included

0.009210852 = product of:
  0.04144883 = sum of:
    0.0128330635 = weight(_text_:of in 1951) [ClassicSimilarity], result of:
      0.0128330635 = score(doc=1951,freq=6.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.20947541 = fieldWeight in 1951, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1951)
    0.028615767 = weight(_text_:systems in 1951) [ClassicSimilarity], result of:
      0.028615767 = score(doc=1951,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.23767869 = fieldWeight in 1951, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1951)
  0.22222222 = coord(2/9)

Abstract

Describes experiments in the retrieval of spoken documents in multimedia systems. Speech documents pose a particular problem for retrieval since their words as well as contents are unknown. Addresses this problem, for a video mail application, by combining state of the art speech recognition with established document retrieval technologies so as to provide an effective and efficient retrieval tool. Tests with a small spoken message collection show that retrieval precision for the spoken file can reach 90% of that obtained when the same file is used, as a benchmark, in text transcription form

0.008178182 = product of:
  0.03680182 = sum of:
    0.016567415 = weight(_text_:of in 3645) [ClassicSimilarity], result of:
      0.016567415 = score(doc=3645,freq=40.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2704316 = fieldWeight in 3645, product of:
          6.3245554 = tf(freq=40.0), with freq of:
            40.0 = termFreq=40.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3645)
    0.020234404 = weight(_text_:systems in 3645) [ClassicSimilarity], result of:
      0.020234404 = score(doc=3645,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.16806422 = fieldWeight in 3645, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.02734375 = fieldNorm(doc=3645)
  0.22222222 = coord(2/9)

Abstract

The selection that follows was chosen as it represents "a very early paper an the possibilities allowed by computers an documentation." In the early 1960s computers were being used to provide simple automatic indexing systems wherein keywords were extracted from documents. The problem with such systems was that they lacked vocabulary control, thus documents related in subject matter were not always collocated in retrieval. To improve retrieval by improving recall is the raison d'être of vocabulary control tools such as classifications and thesauri. The question arose whether it was possible by automatic means to construct classes of terms, which when substituted, one for another, could be used to improve retrieval performance? One of the first theoretical approaches to this question was initiated by R. M. Needham and Karen Sparck Jones at the Cambridge Language Research Institute in England.t The question was later pursued using experimental methodologies by Sparck Jones, who, as a Senior Research Associate in the Computer Laboratory at the University of Cambridge, has devoted her life's work to research in information retrieval and automatic naturai language processing. Based an the principles of numerical taxonomy, automatic classification techniques start from the premise that two objects are similar to the degree that they share attributes in common. When these two objects are keywords, their similarity is measured in terms of the number of documents they index in common. Step 1 in automatic classification is to compute mathematically the degree to which two terms are similar. Step 2 is to group together those terms that are "most similar" to each other, forming equivalence classes of intersubstitutable terms. The technique for forming such classes varies and is the factor that characteristically distinguishes different approaches to automatic classification. The technique used by Needham and Sparck Jones, that of clumping, is described in the selection that follows. Questions that must be asked are whether the use of automatically generated classes really does improve retrieval performance and whether there is a true eco nomic advantage in substituting mechanical for manual labor. Several years after her work with clumping, Sparck Jones was to observe that while it was not wholly satisfactory in itself, it was valuable in that it stimulated research into automatic classification. To this it might be added that it was valuable in that it introduced to libraryl information science the methods of numerical taxonomy, thus stimulating us to think again about the fundamental nature and purpose of classification. In this connection it might be useful to review how automatically derived classes differ from those of manually constructed classifications: 1) the manner of their derivation is purely a posteriori, the ultimate operationalization of the principle of literary warrant; 2) the relationship between members forming such classes is essentially statistical; the members of a given class are similar to each other not because they possess the class-defining characteristic but by virtue of sharing a family resemblance; and finally, 3) automatically derived classes are not related meaningfully one to another, that is, they are not ordered in traditional hierarchical and precedence relationships.

Footnote

Original in: Journal of documentation 20(1964) no.1, S.5-15.

Source

Theory of subject analysis: a sourcebook. Ed.: L.M. Chan, et al

0.00742293 = product of:
  0.033403184 = sum of:
    0.012963352 = weight(_text_:of in 4532) [ClassicSimilarity], result of:
      0.012963352 = score(doc=4532,freq=12.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.21160212 = fieldWeight in 4532, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4532)
    0.020439833 = weight(_text_:systems in 4532) [ClassicSimilarity], result of:
      0.020439833 = score(doc=4532,freq=2.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.1697705 = fieldWeight in 4532, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4532)
  0.22222222 = coord(2/9)

Abstract

This technical note describes straightforward techniques for document indexing and retrieval that have been solidly established through extensive testing and are easy to apply. They are useful for many different types of text material, are viable for very large files, and have the advantage that they do not require special skills or training for searching, but are easy for end users. The document and text retrieval methods described here have a sound theoretical basis, are well established by extensive testing, and the ideas involved are now implemented in some commercial retrieval systems. Testing in the last few years has, in particular, shown that the methods presented here work very well with full texts, not only title and abstracts, and with large files of texts containing three quarters of a million documents. These tests, the TREC Tests (see Harman 1993 - 1997; IP&M 1995), have been rigorous comparative evaluations involving many different approaches to information retrieval. These techniques depend an the use of simple terms for indexing both request and document texts; an term weighting exploiting statistical information about term occurrences; an scoring for request-document matching, using these weights, to obtain a ranked search output; and an relevance feedback to modify request weights or term sets in iterative searching. The normal implementation is via an inverted file organisation using a term list with linked document identifiers, plus counting data, and pointers to the actual texts. The user's request can be a word list, phrases, sentences or extended text.

Issue

May, 1997, Update of 1994 and 1996 versions.

Series

Technical Report TR356, University of Cambridge, Computer Laboratory

0.00642362 = product of:
  0.05781258 = sum of:
    0.05781258 = weight(_text_:systems in 1304) [ClassicSimilarity], result of:
      0.05781258 = score(doc=1304,freq=4.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.48018348 = fieldWeight in 1304, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.078125 = fieldNorm(doc=1304)
  0.11111111 = coord(1/9)

PRECIS

Computer systems / Programming languages / Grammar

Subject

Computer systems / Programming languages / Grammar

0.0063590594 = product of:
  0.057231534 = sum of:
    0.057231534 = weight(_text_:systems in 817) [ClassicSimilarity], result of:
      0.057231534 = score(doc=817,freq=8.0), product of:
        0.12039685 = queryWeight, product of:
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.03917671 = queryNorm
        0.47535738 = fieldWeight in 817, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.0731742 = idf(docFreq=5561, maxDocs=44218)
          0.0546875 = fieldNorm(doc=817)
  0.11111111 = coord(1/9)

Abstract

This article analyzes current trends in information management, considers the problems they involve, and suggests some strategies for tackling these problems. The current goal is integrated, personalized information systems, to be reached via artificial intelligence. The argument is that the extent to which this goal can be achieved is limited because these systems are intrinsically heterogeneous, are for access to information, and deal in linguistically expressed information; so the best strategy for building the systems that can be attained is via linguisticallay oriented knowledge and inference. Evaluating these systems also presents problems because each use is unique, but evaluation is much needed and large-sample strategies for performance study can be devised.

0.002661118 = product of:
  0.023950063 = sum of:
    0.023950063 = weight(_text_:of in 5187) [ClassicSimilarity], result of:
      0.023950063 = score(doc=5187,freq=4.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.39093933 = fieldWeight in 5187, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.125 = fieldNorm(doc=5187)
  0.11111111 = coord(1/9)

Source

Journal of documentation. 28(1972), S.11-21

0.0024947983 = product of:
  0.022453185 = sum of:
    0.022453185 = weight(_text_:of in 4392) [ClassicSimilarity], result of:
      0.022453185 = score(doc=4392,freq=36.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.36650562 = fieldWeight in 4392, product of:
          6.0 = tf(freq=36.0), with freq of:
            36.0 = termFreq=36.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4392)
  0.11111111 = coord(1/9)

Abstract

Purpose - This paper was originally published in 1970 (Journal of documentation. 26(1970), S.89-101), considered the suggestion that classifications for retrieval should be constructed automatically and raised some serious problems concerning the sorts of classification which were required, and the way in which formal classification theories should be exploited, given that a retrieval classification is required for a purpose. These difficulties had not been sufficiently considered, and the paper, therefore, aims to attempt an analysis of them, though no solutions of immediate application could be suggested. Design/methodology/approach - Starting with the illustrative proposition that a polythetic, multiple, unordered classification is required in automatic thesaurus construction, this is considered in the context of classification in general, where eight sorts of classification can be distinguished, each covering a range of class definitions and class-finding algorithms. Findings - Since there is generally no natural or best classification of a set of objects as such, the evaluation of alternative classifications requires either formal criteria of goodness of fit, or, if a classification is required for a purpose, a precise statement of that purpose. In any case a substantive theory of classification is needed, which does not exist; and, since sufficiently precise specifications of retrieval requirements are also lacking, the only currently available approach to automatic classification experiments for information retrieval is to do enough of them. Originality/value - Gives insights into the classification of material for information retrieval.

Source

Journal of documentation. 61(2005) no.5, S.571-581

0.002424508 = product of:
  0.021820573 = sum of:
    0.021820573 = weight(_text_:of in 4327) [ClassicSimilarity], result of:
      0.021820573 = score(doc=4327,freq=34.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.35617945 = fieldWeight in 4327, product of:
          5.8309517 = tf(freq=34.0), with freq of:
            34.0 = termFreq=34.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4327)
  0.11111111 = coord(1/9)

Abstract

The suggestion that classifications for retrieval should be constructed automatically raises some serious problems concerning the sorts of classification which are required, and the way in which formal classification theories should be exploited, given that a retrieval classification is required for a purpose. These difficulties have not been sufficiently considered, and the paper therefore attempts an analysis of them, though no solution of immediate application can be suggested. Starting with the illustrative proposition that a polythetic, multiple, unordered classification is required in automatic thesaurus construction, this is considered in the context of classification in general, where eight sorts of classification can be distinguished, each covering a range of class definitions and class-finding algorithms. The problem which follows is that since there is generally no natural or best classification of a set of objects as such, the evaluation of alternative classifications requires either formal criteria of goodness of fit, or, if a classification is required for a purpose, a precises statement of that purpose. In any case a substantive theory of classification is needed, which does not exist; and since sufficiently precise specifications of retrieval requirements are also lacking, the only currently available approach to automatic classification experiments for information retrieval is to do enough of them

Footnote

Wiederabdruck in: Journal of documentation. 61(2005) no.5, S.571-581.

Source

Journal of documentation. 26(1970), S.89-101

0.0023284785 = product of:
  0.020956306 = sum of:
    0.020956306 = weight(_text_:of in 4420) [ClassicSimilarity], result of:
      0.020956306 = score(doc=4420,freq=16.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.34207192 = fieldWeight in 4420, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4420)
  0.11111111 = coord(1/9)

Abstract

The exhaustivity of document descriptions and the specificity of index terms are usually regarded as independent. It is suggested that specificity should be interpreted statistically, as a function of term use rather than of term meaning. The effects on retrieval of variations in term specificity are examined, experiments with three test collections showing, in particular, that frequently-occurring terms are required for good overall performance. It is argued that terms should be weighted according to collection frequency, so that matches on less frequent, more specific, terms are of greater value than matches on frequent terms. Results for the test collections show that considerable improvements in performance are obtained with this very simple procedure.

Source

Journal of documentation. 60(2004) no.5, S.493-502

0.0018816947 = product of:
  0.016935252 = sum of:
    0.016935252 = weight(_text_:of in 2918) [ClassicSimilarity], result of:
      0.016935252 = score(doc=2918,freq=2.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.27643585 = fieldWeight in 2918, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.125 = fieldNorm(doc=2918)
  0.11111111 = coord(1/9)

Source

Journal of documentation. 40(1984), S.50-66

0.0018816947 = product of:
  0.016935252 = sum of:
    0.016935252 = weight(_text_:of in 5177) [ClassicSimilarity], result of:
      0.016935252 = score(doc=5177,freq=2.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.27643585 = fieldWeight in 5177, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.125 = fieldNorm(doc=5177)
  0.11111111 = coord(1/9)

0.0018816947 = product of:
  0.016935252 = sum of:
    0.016935252 = weight(_text_:of in 1916) [ClassicSimilarity], result of:
      0.016935252 = score(doc=1916,freq=8.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.27643585 = fieldWeight in 1916, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0625 = fieldNorm(doc=1916)
  0.11111111 = coord(1/9)

Abstract

Discusses the TREC programme as a major enterprise in information retrieval research. It reviews its structure as an evaluation exercise, characterises the methods of indexing and retrieval being tested within it in terms of the approaches to system performance factors these represent; analyses the test results for solid, overall conclusions that can be drawn from them; and, in the light of the particular features of the test data, assesses TREC both for generally applicable findings that emerge from it and for directions it offers for future research

0.0018816947 = product of:
  0.016935252 = sum of:
    0.016935252 = weight(_text_:of in 250) [ClassicSimilarity], result of:
      0.016935252 = score(doc=250,freq=2.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.27643585 = fieldWeight in 250, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.125 = fieldNorm(doc=250)
  0.11111111 = coord(1/9)

Source

Annual review of information science and technology. 6(1971), S.141-166

0.0018408239 = product of:
  0.016567415 = sum of:
    0.016567415 = weight(_text_:of in 4161) [ClassicSimilarity], result of:
      0.016567415 = score(doc=4161,freq=10.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.2704316 = fieldWeight in 4161, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4161)
  0.11111111 = coord(1/9)

Abstract

Many retrieval experiments have been based on inadequate test collections, and current research is hampered by the lack of proper collections. This short review does not attempt a fully docuemted survey of all the collections used in the past decade: hopefully representative examples have been studied to throw light on the requriements test collections should meet, to show how past collections have been defective, and to suggest guidelines for a future "ideal" test collection. This specifications for this collection can be taken as an indirect comment on our present state of knowledge of major retrieval system variables, and experience in conducting experiments.

Source

Journal of documentation. 32(1976) no.1, S.59-75

0.0016631988 = product of:
  0.014968789 = sum of:
    0.014968789 = weight(_text_:of in 4422) [ClassicSimilarity], result of:
      0.014968789 = score(doc=4422,freq=4.0), product of:
        0.061262865 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.03917671 = queryNorm
        0.24433708 = fieldWeight in 4422, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.078125 = fieldNorm(doc=4422)
  0.11111111 = coord(1/9)

Abstract

Robertson comments on the theoretical status of IDF term weighting. Its history illustrates how ideas develop in a specific research context, in theory/experiment interaction, and in operational practice.

Source

Journal of documentation. 60(2004) no.5, S.521-523