Search (135 results, page 1 of 7)

0.015412329 = product of:
  0.061649315 = sum of:
    0.061649315 = weight(_text_:digitale in 942) [ClassicSimilarity], result of:
      0.061649315 = score(doc=942,freq=2.0), product of:
        0.18027179 = queryWeight, product of:
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.034944877 = queryNorm
        0.34197983 = fieldWeight in 942, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.158747 = idf(docFreq=690, maxDocs=44218)
          0.046875 = fieldNorm(doc=942)
  0.25 = coord(1/4)

Abstract

Der Workshop gibt einen Einblick in die aktuelle Forschung und Entwicklung zur Wissensorganisation in digitalen Bibliotheken. Diane Vizine-Goetz vom OCLC Office of Research in Dublin, Ohio, stellt die Forschungsprojekte von OCLC zur Anpassung und Weiterentwicklung der Dewey Decimal Classification als Wissensorganisationsinstrument fuer grosse digitale Dokumentensammlungen vor. Traugott Koch, NetLab, Universität Lund in Schweden, demonstriert die Ansätze und Lösungen des EU-Projekts DESIRE zum Einsatz von intellektueller und vor allem automatischer Klassifikation in Fachinformationsdiensten im Internet.

0.006182458 = product of:
  0.024729831 = sum of:
    0.024729831 = weight(_text_:information in 382) [ClassicSimilarity], result of:
      0.024729831 = score(doc=382,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.40312737 = fieldWeight in 382, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=382)
  0.25 = coord(1/4)

Imprint

Hinskey Hill : Learned Information

Source

Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al

0.0051520485 = product of:
  0.020608194 = sum of:
    0.020608194 = weight(_text_:information in 494) [ClassicSimilarity], result of:
      0.020608194 = score(doc=494,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.3359395 = fieldWeight in 494, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.078125 = fieldNorm(doc=494)
  0.25 = coord(1/4)

Imprint

Hinskey Hill : Learned Information

Source

Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al

0.0051002675 = product of:
  0.02040107 = sum of:
    0.02040107 = weight(_text_:information in 1071) [ClassicSimilarity], result of:
      0.02040107 = score(doc=1071,freq=12.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.3325631 = fieldWeight in 1071, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1071)
  0.25 = coord(1/4)

Abstract

A fuzzy multiset model for information clustering is proposed with application to information retrieval on the World Wide Web. Noting that a search engine retrieves multiple occurrences of the same subjects with possibly different degrees of relevance, we observe that fuzzy multisets provide an appropriate model of information retrieval on the WWW. Information clustering which means both term clustering and document clustering is considered. Three methods of the hard c-means, fuzzy c-means, and an agglomerative method using cluster centers are proposed. Two distances between fuzzy multisets and algorithms for calculating cluster centers are defined. Theoretical properties concerning the clustering algorithms are studied. Illustrative examples are given to show how the algorithms work.

Source

Information processing and management. 39(2003) no.2, S.195-213

0.0050479556 = product of:
  0.020191822 = sum of:
    0.020191822 = weight(_text_:information in 2339) [ClassicSimilarity], result of:
      0.020191822 = score(doc=2339,freq=16.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.3291521 = fieldWeight in 2339, product of:
          4.0 = tf(freq=16.0), with freq of:
            16.0 = termFreq=16.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=2339)
  0.25 = coord(1/4)

Abstract

Text classification (TC) is a core technique for text mining and information retrieval. It has been applied to many applications in many different research and industrial areas. Term-weighting schemes assign an appropriate weight to each term to obtain a high TC performance. Although term weighting is one of the important modules for TC and TC has different peculiarities from those in information retrieval, many term-weighting schemes used in information retrieval, such as term frequency-inverse document frequency (tf-idf), have been used in TC in the same manner. The peculiarity of TC that differs most from information retrieval is the existence of class information. This article proposes a new term-weighting scheme that uses class information using positive and negative class distributions. As a result, the proposed scheme, log tf-TRR, consistently performs better than do other schemes using class information as well as traditional schemes such as tf-idf.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2553-2565

0.004759258 = product of:
  0.019037032 = sum of:
    0.019037032 = weight(_text_:information in 2412) [ClassicSimilarity], result of:
      0.019037032 = score(doc=2412,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.3103276 = fieldWeight in 2412, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.125 = fieldNorm(doc=2412)
  0.25 = coord(1/4)

0.004164351 = product of:
  0.016657405 = sum of:
    0.016657405 = weight(_text_:information in 4347) [ClassicSimilarity], result of:
      0.016657405 = score(doc=4347,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.27153665 = fieldWeight in 4347, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=4347)
  0.25 = coord(1/4)

Source

Information processing and management. 11(1975), S.201-206

0.004164351 = product of:
  0.016657405 = sum of:
    0.016657405 = weight(_text_:information in 4846) [ClassicSimilarity], result of:
      0.016657405 = score(doc=4846,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.27153665 = fieldWeight in 4846, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=4846)
  0.25 = coord(1/4)

Source

Information storage and retrieval. 6(1971), S.417-435

0.004164351 = product of:
  0.016657405 = sum of:
    0.016657405 = weight(_text_:information in 2666) [ClassicSimilarity], result of:
      0.016657405 = score(doc=2666,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.27153665 = fieldWeight in 2666, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.109375 = fieldNorm(doc=2666)
  0.25 = coord(1/4)

Source

Information processing and management. 37(2001) no.3, S.459-484

0.004121639 = product of:
  0.016486555 = sum of:
    0.016486555 = weight(_text_:information in 2564) [ClassicSimilarity], result of:
      0.016486555 = score(doc=2564,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2687516 = fieldWeight in 2564, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=2564)
  0.25 = coord(1/4)

Abstract

The classification of documents from a bibliographic database is a task that is linked to processes of information retrieval based on partial matching. A method is described of vectorizing reference documents from LISA which permits their topological organization using Kohonen's algorithm. As an example a map is generated of 202 documents from LISA, and an analysis is made of the possibilities of this type of neural network with respect to the development of information retrieval systems based on graphical browsing.

Source

Information processing and management. 38(2002) no.1, S.79-89

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 316) [ClassicSimilarity], result of:
      0.015963038 = score(doc=316,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 316, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=316)
  0.25 = coord(1/4)

Abstract

Information retrieval over the Internet increasingly requires the filtering of thousands of heterogeneous information sources. Important sources of information include not only traditional databases with structured data and queries, but also increasing numbers of non-traditional, semi- or unstructured collections such as Web sites, FTP archives, etc. As the number and variability of sources increases, new ways of automatically summarizing, discovering, and selecting collections relevant to a user's query are needed. One such method involves the use of classification schemes, such as the Library of Congress Classification (LCC) [10], within which a collection may be represented based on its content, irrespective of the structure of the actual data or documents. For such a system to be useful in a large-scale distributed environment, it must be easy to use for both collection managers and users. As a result, it must be possible to classify documents automatically within a classification scheme. Furthermore, there must be a straightforward and intuitive interface with which the user may use the scheme to assist in information retrieval (IR).

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 1755) [ClassicSimilarity], result of:
      0.015963038 = score(doc=1755,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 1755, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1755)
  0.25 = coord(1/4)

Abstract

There has been considerable interest in recent years in providing automated information services, such as information classification, by means of a society of collaborative agents. These agents augment each other's knowledge structures (e.g., the vocabularies) and assist each other in providing efficient information services to a human user. However, when the number of agents present in the society increases, exhaustive communication and collaboration among agents result in a [arge communication overhead and increased delays in response time. This paper introduces a method to achieve selective interaction with a relatively small number of potentially useful agents, based an simple agent modeling and acquaintance lists. The key idea presented here is that the acquaintance list of an agent, representing a small number of other agents to be collaborated with, is dynamically adjusted. The best acquaintances are automatically discovered using a learning algorithm, based an the past history of collaboration. Experimental results are presented to demonstrate that such dynamically learned acquaintance lists can lead to high quality of classification, while significantly reducing the delay in response time.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.10, S.966-975

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 995) [ClassicSimilarity], result of:
      0.015963038 = score(doc=995,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 995, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=995)
  0.25 = coord(1/4)

Abstract

Recently, a new community has started to emerge around the development of new information research methods for searching and analyzing semi-structured and XML like documents. The goal is to handle both content and structural information, and to deal with different types of information content (text, image, etc.). We consider here the task of structured document classification. We propose a generative model able to handle both structure and content which is based on Bayesian networks. We then show how to transform this generative model into a discriminant classifier using the method of Fisher kernel. The model is then extended for dealing with different types of content information (here text and images). The model was tested on three databases: the classical webKB corpus composed of HTML pages, the new INEX corpus which has become a reference in the field of ad-hoc retrieval for XML documents, and a multimedia corpus of Web pages.

Source

Information processing and management. 40(2004) no.5, S.807-827

0.0039907596 = product of:
  0.015963038 = sum of:
    0.015963038 = weight(_text_:information in 1998) [ClassicSimilarity], result of:
      0.015963038 = score(doc=1998,freq=10.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.2602176 = fieldWeight in 1998, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.046875 = fieldNorm(doc=1998)
  0.25 = coord(1/4)

Abstract

Since millions seek health information online, it is vital for this information to be comprehensible. Most studies use readability formulas, which ignore vocabulary, and conclude that online health information is too difficult. We developed a vocabularly-based, naïve Bayes classifier to distinguish between three difficulty levels in text. It proved 98% accurate in a 250-document evaluation. We compared our classifier with readability formulas for 90 new documents with different origins and asked representative human evaluators, an expert and a consumer, to judge each document. Average readability grade levels for educational and commercial pages was 10th grade or higher, too difficult according to current literature. In contrast, the classifier showed that 70-90% of these pages were written at an intermediate, appropriate level indicating that vocabulary usage is frequently appropriate in text considered too difficult by readability formula evaluations. The expert considered the pages more difficult for a consumer than the consumer did.

Source

Journal of the American Society for Information Science and Technology. 59(2008) no.9, S.1409-1419

0.0039349417 = product of:
  0.015739767 = sum of:
    0.015739767 = weight(_text_:information in 1107) [ClassicSimilarity], result of:
      0.015739767 = score(doc=1107,freq=14.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.256578 = fieldWeight in 1107, product of:
          3.7416575 = tf(freq=14.0), with freq of:
            14.0 = termFreq=14.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1107)
  0.25 = coord(1/4)

Abstract

Retrieval of disease information is often based on several key aspects such as etiology, diagnosis, treatment, prevention, and symptoms of diseases. Automatic identification of disease aspect information is thus essential. In this article, I model the aspect identification problem as a text classification (TC) problem in which a disease aspect corresponds to a category. The disease aspect classification problem poses two challenges to classifiers: (a) a medical text often contains information about multiple aspects of a disease and hence produces noise for the classifiers and (b) text classifiers often cannot extract the textual parts (i.e., passages) about the categories of interest. I thus develop a technique, PETC (Passage Extractor for Text Classification), that extracts passages (from medical texts) for the underlying text classifiers to classify. Case studies on thousands of Chinese and English medical texts show that PETC enhances a support vector machine (SVM) classifier in classifying disease aspect information. PETC also performs better than three state-of-the-art classifier enhancement techniques, including two passage extraction techniques for text classifiers and a technique that employs term proximity information to enhance text classifiers. The contribution is of significance to evidence-based medicine, health education, and healthcare decision support. PETC can be used in those application domains in which a text to be classified may have several parts about different categories.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2265-2277

0.003606434 = product of:
  0.014425736 = sum of:
    0.014425736 = weight(_text_:information in 2338) [ClassicSimilarity], result of:
      0.014425736 = score(doc=2338,freq=6.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23515764 = fieldWeight in 2338, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2338)
  0.25 = coord(1/4)

Imprint

Urbana-Champaign, IL : Illinois University at Urbana-Champaign, Graduate School of Library and Information Science

Source

Visualizing subject access for 21st century information resources: Papers presented at the 1997 Clinic on Library Applications of Data Processing, 2-4 Mar 1997, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign. Ed.: P.A. Cochrane et al

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 3789) [ClassicSimilarity], result of:
      0.014277775 = score(doc=3789,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 3789, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=3789)
  0.25 = coord(1/4)

Source

Information processing and management. 36(2000) no.4, S.683-696

0.0035694437 = product of:
  0.014277775 = sum of:
    0.014277775 = weight(_text_:information in 4084) [ClassicSimilarity], result of:
      0.014277775 = score(doc=4084,freq=2.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.23274569 = fieldWeight in 4084, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.09375 = fieldNorm(doc=4084)
  0.25 = coord(1/4)

Source

SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a

0.0033653039 = product of:
  0.013461215 = sum of:
    0.013461215 = weight(_text_:information in 2650) [ClassicSimilarity], result of:
      0.013461215 = score(doc=2650,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.21943474 = fieldWeight in 2650, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=2650)
  0.25 = coord(1/4)

Abstract

The use of terms from natural and social science titles and abstracts is studied from the perspective of sublanguages and their specialized dictionaries. Explores different notions of sublanguage distinctiveness. Object methods for separating hard and soft sciences are suggested based on measures of sublanguage use, dictionary characteristics, and sublanguage distinctiveness. Abstracts were automatically classified with a high degree of accuracy by using a formula that condsiders the degree of uniqueness of terms in each sublanguage. This may prove useful for text filtering of information retrieval systems

Source

Journal of the American Society for Information Science. 46(1995) no.7, S.519-529

0.0033653039 = product of:
  0.013461215 = sum of:
    0.013461215 = weight(_text_:information in 7695) [ClassicSimilarity], result of:
      0.013461215 = score(doc=7695,freq=4.0), product of:
        0.06134496 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.034944877 = queryNorm
        0.21943474 = fieldWeight in 7695, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0625 = fieldNorm(doc=7695)
  0.25 = coord(1/4)

Abstract

Examnines Ranganathan's approach to knowledge organisation and its relevance to intellectual accessibility in libraries. Discusses the current and future developments of his methodology and theories in knowledge-based systems. Topics covered include: semi-automatic classification and structure of thesauri; user-intermediary interactions in information retrieval (IR); semantic value-theory and uncertainty principles in IR; and case grammar