Search (354 results, page 17 of 18)

2.3021935E-4 = product of:
  0.00345329 = sum of:
    0.00345329 = product of:
      0.00690658 = sum of:
        0.00690658 = weight(_text_:information in 718) [ClassicSimilarity], result of:
          0.00690658 = score(doc=718,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.13576832 = fieldWeight in 718, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0546875 = fieldNorm(doc=718)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

In the face of the ever-increasing document volume, libraries around the globe are more and more exploring (semi-) automated approaches to subject indexing. This helps sustain bibliographic objectives, enrich metadata, and establish more connections across documents from various collections, effectively leading to improved information retrieval and access. However, generally accepted automated approaches that are functional in operative systems are lacking. This article aims to provide an overview of basic principles used for automated subject indexing, major approaches in relation to their possible application in actual library systems, existing working examples, as well as related challenges calling for further research.

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 4999) [ClassicSimilarity], result of:
          0.005919926 = score(doc=4999,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 4999, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=4999)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Microcomputers for information management. 7(1990) no. 2, S.127-148

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 6497) [ClassicSimilarity], result of:
          0.005919926 = score(doc=6497,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 6497, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=6497)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the American Society for Information Science. 48(1997) no.1, S.55-66

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 161) [ClassicSimilarity], result of:
          0.005919926 = score(doc=161,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 161, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=161)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the American Society for Information Science. 48(1997) no.5, S.395-417

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 704) [ClassicSimilarity], result of:
          0.005919926 = score(doc=704,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 704, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=704)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

Considers the challenge to professional indexers posed by the Internet. Indexing and searching on the Internet appears to have a retrograde step, as well developed and efficient information retrieval techniques have been replaced by cruder techniques, involving automatic keyword indexing and frequency ranking, leading to large retrieval sets and low precision. This is made worse by the apparent acceptance of this poor perfromance by Internet users and the feeling, on the part of indexers, that they are being bypassed by the producers of these hyperlinked menus and search engines. Key issues are: how far 'human' indexing will still be required in the Internet environment; how indexing techniques will have to change to stay relevant; and the future role of indexers. The challenge facing indexers is to adapt their skills to suit the online environment and to convince publishers of the need for efficient indexes on the Internet

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 3769) [ClassicSimilarity], result of:
          0.005919926 = score(doc=3769,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 3769, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3769)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the American Society for Information Science. 50(1999) no.8, S.661-674

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 711) [ClassicSimilarity], result of:
          0.005919926 = score(doc=711,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 711, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=711)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

This article discusses the possibility of the automation of sophisticated subject indexing of medical journal articles. Approaches to subject descriptor assignment in information retrieval research are usually either based upon the manual descriptors in the database or generation of search parameters from the text of the article. The principles of the Medline indexing system are described, followed by a summary of a pilot project, based upon the Amed database. The results suggest that a more extended study, based upon Medline, should encompass various components: Extraction of 'concept strings' from titles and abstracts of records, based upon linguistic features characteristic of medical literature. Use of the Unified Medical Language System (UMLS) for identification of controlled vocabulary descriptors. Coordination of descriptors, utilising features of the Medline indexing system. The emphasis should be on system manipulation of data, based upon input, available resources and specifically designed rules.

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 3422) [ClassicSimilarity], result of:
          0.005919926 = score(doc=3422,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 3422, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3422)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.3, S.522-542

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 3320) [ClassicSimilarity], result of:
          0.005919926 = score(doc=3320,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 3320, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=3320)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the Association for Information Science and Technology. 68(2017) no.1, S.141-153

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 412) [ClassicSimilarity], result of:
          0.005919926 = score(doc=412,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 412, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=412)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1511-1527

1.9733087E-4 = product of:
  0.002959963 = sum of:
    0.002959963 = product of:
      0.005919926 = sum of:
        0.005919926 = weight(_text_:information in 723) [ClassicSimilarity], result of:
          0.005919926 = score(doc=723,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.116372846 = fieldWeight in 723, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046875 = fieldNorm(doc=723)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

1.8604532E-4 = product of:
  0.0027906797 = sum of:
    0.0027906797 = product of:
      0.0055813594 = sum of:
        0.0055813594 = weight(_text_:information in 2596) [ClassicSimilarity], result of:
          0.0055813594 = score(doc=2596,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.10971737 = fieldWeight in 2596, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=2596)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Content

Ramana Rao (Inxight, Palo Alto, CA) 7 ± 2 Insights on achieving Effective Information Access Session One: Updates and a twelve month perspective Danny Sullivan (Search Engine Watch, US / England) Portalization and other search trends Carol Tenopir (University of Tennessee) Search realities faced by end users and professional searchers Session Two: Today's search engines and beyond Daniel Hoogterp (Retrieval Technologies, McLean, VA) Effective presentation and utilization of search techniques Rick Kenny (Fulcrum Technologies, Ontario, Canada) Beyond document clustering: The knowledge impact statement Gary Stock (Ingenius, Kalamazoo, MI) Automated change monitoring Gary Culliss (Direct Hit, Wellesley Hills, MA) User popularity ranked search engines Byron Dom (IBM, CA) Automatically finding the best pages on the World Wide Web (CLEVER) Peter Tomassi (LookSmart, San Francisco, CA) Adding human intellect to search technology Session Three: Panel discussion: Human v automated categorization and editing Ev Brenner (New York, NY)- Chairman James Callan (University of Massachusetts, MA) Marc Krellenstein (Northern Light Technology, Cambridge, MA) Dan Miller (Ask Jeeves, Berkeley, CA) Session Four: Updates and a twelve month perspective Steve Arnold (AIT, Harrods Creek, KY) Review: The leading edge in search and retrieval software Ellen Voorhees (NIST, Gaithersburg, MD) TREC update Session Five: Search engines now and beyond Intelligent Agents John Snyder (Muscat, Cambridge, England) Practical issues behind intelligent agents Text summarization Therese Firmin, (Dept of Defense, Ft George G. Meade, MD) The TIPSTER/SUMMAC evaluation of automatic text summarization systems Cross language searching Elizabeth Liddy (TextWise, Syracuse, NY) A conceptual interlingua approach to cross-language retrieval. Video search and retrieval Armon Amir (IBM, Almaden, CA) CueVideo: Modular system for automatic indexing and browsing of video/audio Speech recognition Michael Witbrock (Lycos, Waltham, MA) Retrieval of spoken documents Visualization James A. Wise (Integral Visuals, Richland, WA) Information visualization in the new millennium: Emerging science or passing fashion? Text mining David Evans (Claritech, Pittsburgh, PA) Text mining - towards decision support

1.8604532E-4 = product of:
  0.0027906797 = sum of:
    0.0027906797 = product of:
      0.0055813594 = sum of:
        0.0055813594 = weight(_text_:information in 1264) [ClassicSimilarity], result of:
          0.0055813594 = score(doc=1264,freq=4.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.10971737 = fieldWeight in 1264, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.03125 = fieldNorm(doc=1264)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase-level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two-thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self-formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2-second response times at the highest loads tested).

Source

Journal of the Association for Information Science and Technology. 65(2014) no.5, S.1058-1075

1.6444239E-4 = product of:
  0.0024666358 = sum of:
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 2849) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=2849,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 2849, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2849)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the American Society for Information Science. 38(1987) no.4, S.269-287

1.6444239E-4 = product of:
  0.0024666358 = sum of:
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 4144) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=4144,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 4144, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4144)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

Indexing documents with descriptors from a multilingual thesaurus is an approach to multilingual information retrieval. However, manual indexing is expensive. Automazed indexing methods in general use terms found in the document. Thesaurus descriptors are complex terms that are often not used in documents or have specific meanings within the thesaurus; therefore most weighting schemes of automated indexing methods are not suited to select thesaurus descriptors. In this paper a linear associative system is described that uses similarity values extracted from a large corpus of manually indexed documents to construct a rank ordering of the descriptors for a given document title. The system is adaptive and has to be tuned with a training sample of records for the specific task. The system was tested on a corpus of some 80.000 bibliographic records. The results show a high variability with changing parameter values. This indicated that it is very important to empirically adapt the model to the specific situation it is used in. The overall median of the manually assigned descriptors in the automatically generated ranked list of all 3.631 descriptors is 14 for the set used to adapt the system and 11 for a test set not used in the optimization process. This result shows that the optimization is not a fitting to a specific training set but a real adaptation of the model to the setting

1.6444239E-4 = product of:
  0.0024666358 = sum of:
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 601) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=601,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 601, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=601)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Journal of the American Society for Information Science and technology. 53(2002) no.8, S.653-677

1.6444239E-4 = product of:
  0.0024666358 = sum of:
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 2918) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=2918,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 2918, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2918)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Abstract

Purpose - The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval. Design/methodology/approach - Over 11.000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as suggestions from a controlled vocabulary. The controlled vocabulary was DDC comprising also mappings from the Library of Congress Subject Headings. Findings - The results demonstrate the importance of controlled vocabulary suggestions for indexing and retrieval: to help produce ideas of which tags to use, to make it easier to find focus for the tagging, to ensure consistency and to increase the number of access points in retrieval. The value and usefulness of the suggestions proved to be dependent on the quality of the suggestions, both as to conceptual relevance to the user and as to appropriateness of the terminology. Originality/value - No research has investigated the enhancement of social tagging with suggestions from the DDC, an established KOS, in a user trial, comparing social tagging only and social tagging enhanced with the suggestions. This paper is a final reflection on all aspects of the study.

1.6444239E-4 = product of:
  0.0024666358 = sum of:
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 896) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=896,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 896, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=896)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Information processing and management. 43(2007) no.1, S.81-102

1.6444239E-4 = product of:
  0.0024666358 = sum of:
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 963) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=963,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 963, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=963)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Source

Information processing and management. 42(2006) no.1, S.136-154

1.6444239E-4 = product of:
  0.0024666358 = sum of:
    0.0024666358 = product of:
      0.0049332716 = sum of:
        0.0049332716 = weight(_text_:information in 1518) [ClassicSimilarity], result of:
          0.0049332716 = score(doc=1518,freq=2.0), product of:
            0.050870337 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.028978055 = queryNorm
            0.09697737 = fieldWeight in 1518, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1518)
      0.5 = coord(1/2)
  0.06666667 = coord(1/15)

Content

Automatic Text Summarization Some Important Concepts 23 Single document Summarization 53 Guided Multi-Document Summarization 109 Emerging systems 151 Source and DomainSpecific Summarization 179 Text Abstracting 219 Evaluating Document Summaries 243 Conclusion 275 Information Retrieval NLP and Automatic Text Summarization 281 Automatic Text Summarization Resources 305