Search (101 results, page 2 of 6)

0.016370157 = product of:
  0.04911047 = sum of:
    0.037074894 = weight(_text_:computer in 5499) [ClassicSimilarity], result of:
      0.037074894 = score(doc=5499,freq=4.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.22840683 = fieldWeight in 5499, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.03125 = fieldNorm(doc=5499)
    0.012035574 = product of:
      0.024071148 = sum of:
        0.024071148 = weight(_text_:22 in 5499) [ClassicSimilarity], result of:
          0.024071148 = score(doc=5499,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.15476047 = fieldWeight in 5499, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03125 = fieldNorm(doc=5499)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)

Abstract

Purpose Modern mathematicians and scientists of math-related disciplines often use Document Preparation Systems (DPS) to write and Computer Algebra Systems (CAS) to calculate mathematical expressions. Usually, they translate the expressions manually between DPS and CAS. This process is time-consuming and error-prone. The purpose of this paper is to automate this translation. This paper uses Maple and Mathematica as the CAS, and LaTeX as the DPS. Design/methodology/approach Bruce Miller at the National Institute of Standards and Technology (NIST) developed a collection of special LaTeX macros that create links from mathematical symbols to their definitions in the NIST Digital Library of Mathematical Functions (DLMF). The authors are using these macros to perform rule-based translations between the formulae in the DLMF and CAS. Moreover, the authors develop software to ease the creation of new rules and to discover inconsistencies. Findings The authors created 396 mappings and translated 58.8 percent of DLMF formulae (2,405 expressions) successfully between Maple and DLMF. For a significant percentage, the special function definitions in Maple and the DLMF were different. An atomic symbol in one system maps to a composite expression in the other system. The translator was also successfully used for automatic verification of mathematical online compendia and CAS. The evaluation techniques discovered two errors in the DLMF and one defect in Maple. Originality/value This paper introduces the first translation tool for special functions between LaTeX and CAS. The approach improves error-prone manual translations and can be used to verify mathematical online compendia and CAS.

Date

20. 1.2015 18:30:22

0.015447874 = product of:
  0.09268724 = sum of:
    0.09268724 = weight(_text_:computer in 1307) [ClassicSimilarity], result of:
      0.09268724 = score(doc=1307,freq=4.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.5710171 = fieldWeight in 1307, 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=1307)
  0.16666667 = coord(1/6)

Series

Addison-Wesley series in computer science

0.015292614 = product of:
  0.09175568 = sum of:
    0.09175568 = weight(_text_:computer in 1302) [ClassicSimilarity], result of:
      0.09175568 = score(doc=1302,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.56527805 = fieldWeight in 1302, 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=1302)
  0.16666667 = coord(1/6)

0.01337825 = product of:
  0.0802695 = sum of:
    0.0802695 = weight(_text_:computer in 341) [ClassicSimilarity], result of:
      0.0802695 = score(doc=341,freq=12.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.4945153 = fieldWeight in 341, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=341)
  0.16666667 = coord(1/6)

Abstract

Purpose Based on the highlights of The Metropolitan Museum of Art's collection, the purpose of this paper is to examine the similarities and differences between the subject keywords tags assigned by the museum and those produced by three computer vision systems. Design/methodology/approach This paper uses computer vision tools to generate the data and the Getty Research Institute's Art and Architecture Thesaurus (AAT) to compare the subject keyword tags. Findings This paper finds that there are clear opportunities to use computer vision technologies to automatically generate tags that expand the terms used by the museum. This brings a new perspective to the collection that is different from the traditional art historical one. However, the study also surfaces challenges about the accuracy and lack of context within the computer vision results. Practical implications This finding has important implications on how these machine-generated tags complement the current taxonomies and vocabularies inputted in the collection database. In consequence, the museum needs to consider the selection process for choosing which computer vision system to apply to their collection. Furthermore, they also need to think critically about the kind of tags they wish to use, such as colors, materials or objects. Originality/value The study results add to the rapidly evolving field of computer vision within the art information context and provide recommendations of aspects to consider before selecting and implementing these technologies.

0.012845224 = product of:
  0.07707134 = sum of:
    0.07707134 = weight(_text_:wide in 6961) [ClassicSimilarity], result of:
      0.07707134 = score(doc=6961,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.3916274 = fieldWeight in 6961, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0625 = fieldNorm(doc=6961)
  0.16666667 = coord(1/6)

Abstract

Briefly describes the Excalibur EFS system which makes use of adaptive pattern recognition technology as an aid to automatic indexing and how it is being tested at the British Library for the indexing and retrieval of scanned images from the library's holdings. Notes how Excalibur EFS can support a wide degree of fuzzy searching, compensate for the errors produced by OCR conversion of scanned images, reduce the costs of indexing, and require far less storage space than more traditional indexes

0.0112395715 = product of:
  0.067437425 = sum of:
    0.067437425 = weight(_text_:wide in 3516) [ClassicSimilarity], result of:
      0.067437425 = score(doc=3516,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.342674 = fieldWeight in 3516, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3516)
  0.16666667 = coord(1/6)

Abstract

Report of a study comparing 2 methods of indexing: LEXINET, a computerised system for indexing titles and summaries only; and manual indexing of full texts, using the thesaurus developed by French Electricity (EDF). Both systems were applied to a collection of approximately 2.000 documents on artifical intelligence from the EDF data base. The results were then analysed to compare quantitative performance (number and range of terms) and qualitative performance (ambiguity of terms, specificity, variability, consistency). Overall, neither system proved ideal: LEXINET was deficient as regards lack of accessibility and excessive ambiguity; while the manual system gave rise to an over-wide variation of terms. The ideal system would appear to be a combination of automatic and manual systems, on the evidence produced here.

0.010923296 = product of:
  0.06553978 = sum of:
    0.06553978 = weight(_text_:computer in 2607) [ClassicSimilarity], result of:
      0.06553978 = score(doc=2607,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.40377006 = fieldWeight in 2607, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.078125 = fieldNorm(doc=2607)
  0.16666667 = coord(1/6)

Abstract

The AIDA project is a research program being carried out by Computer Power in Canberra, Australia, in collaboration with the Australian Parliament. Its primary objective is to develop practical methods for carrying out document content analysis with minimal human intervention. The different techniques employed by AIDA to achieve its results are described

0.010923296 = product of:
  0.06553978 = sum of:
    0.06553978 = weight(_text_:computer in 4890) [ClassicSimilarity], result of:
      0.06553978 = score(doc=4890,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.40377006 = fieldWeight in 4890, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.078125 = fieldNorm(doc=4890)
  0.16666667 = coord(1/6)

Abstract

Describes full text retrieval (FTR) which indexes every occurrence of every word except defined 'stop' words. This permits much more sophisticated searching than with keyword indexing. Also discusses document imaging processing (DIP). Lists suppliers and users of the software and describes the experiences of ESOO's Planning Division with Computer Intertrade Ltd. (CIL) ImagePro DIP and their operational practices

0.009717524 = product of:
  0.058305144 = sum of:
    0.058305144 = product of:
      0.11661029 = sum of:
        0.11661029 = weight(_text_:programs in 3105) [ClassicSimilarity], result of:
          0.11661029 = score(doc=3105,freq=4.0), product of:
            0.25748047 = queryWeight, product of:
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.044416238 = queryNorm
            0.45288983 = fieldWeight in 3105, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3105)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Abstract

This paper explores the possible role of named entities in an automatic index-ing process, based on text in subtitles. This is done by analyzing entity types, name den-sity and name frequencies in subtitles and metadata records from different TV programs. The name density in metadata records is much higher than the name density in subtitles, and named entities with high frequencies in the subtitles are more likely to be mentioned in the metadata records. Personal names, geographical names and names of organizations where the most prominent entity types in both the news subtitles and news metadata, while persons, works and locations are the most prominent in culture programs.

0.009633917 = product of:
  0.057803504 = sum of:
    0.057803504 = weight(_text_:wide in 5480) [ClassicSimilarity], result of:
      0.057803504 = score(doc=5480,freq=2.0), product of:
        0.19679762 = queryWeight, product of:
          4.4307585 = idf(docFreq=1430, maxDocs=44218)
          0.044416238 = queryNorm
        0.29372054 = fieldWeight in 5480, 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=5480)
  0.16666667 = coord(1/6)

Abstract

(Automatic) document classification is generally defined as content-based assignment of one or more predefined categories to documents. Usually, machine learning, statistical pattern recognition, or neural network approaches are used to construct classifiers automatically. In this paper we thoroughly evaluate a wide variety of these methods on a document classification task for German text. We evaluate different feature construction and selection methods and various classifiers. Our main results are: (1) feature selection is necessary not only to reduce learning and classification time, but also to avoid overfitting (even for Support Vector Machines); (2) surprisingly, our morphological analysis does not improve classification quality compared to a letter 5-gram approach; (3) Support Vector Machines are significantly better than all other classification methods

0.008738637 = product of:
  0.05243182 = sum of:
    0.05243182 = weight(_text_:computer in 2323) [ClassicSimilarity], result of:
      0.05243182 = score(doc=2323,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.32301605 = fieldWeight in 2323, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=2323)
  0.16666667 = coord(1/6)

Abstract

Es wird zunächst ein "ideales Information-Retrieval-System" beschrieben und diskutiert. Als Kernproblem für -selbst bescheidene - Entwicklungen in die dadurch aufgezeigte Richtung wird das "Verstehen" von Texten durch den Computer angesehen, wobei je nach der Aufgabenstellung einer Systemkomponente stets nur ein partielles Verstehen erforderlich ist. Ein relativ einfaches, aber keineswegs triviales Beispiel dieser Art ist die automatische Indexierung von Referatetexten bei vorgegebenen Deskriptorensystem. Von diesem Problem werden Ansätze, Ergebnisse und Erfahrungen mitgeteilt. Darauf aufbauend werden weitere Forschungsrichtungen und Entwicklungsmöglichkeiten mitgeteilt

0.008738637 = product of:
  0.05243182 = sum of:
    0.05243182 = weight(_text_:computer in 4423) [ClassicSimilarity], result of:
      0.05243182 = score(doc=4423,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.32301605 = fieldWeight in 4423, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=4423)
  0.16666667 = coord(1/6)

Abstract

The ESPRIT project SIMPR developed software to analyse documents and generate indexes for them. Of immediate application as a document indexing and classification system, this also offers a technology for information modelling that has broader implications, supporting many new uses for information management softeware. The project was based on the assumption that information can only be managed successfully by computer systems that can view the information contained in a document through the language in which the document is written, and that systems need to be sufficiently flexible to respond to the changing requirements of document use

0.008738637 = product of:
  0.05243182 = sum of:
    0.05243182 = weight(_text_:computer in 4891) [ClassicSimilarity], result of:
      0.05243182 = score(doc=4891,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.32301605 = fieldWeight in 4891, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=4891)
  0.16666667 = coord(1/6)

Abstract

Describes a method developed for automatic identification of sublanguage vocabulary words as they occur in abstracts. Describes the sublanguage vocabulary identification procedures using abstracts from computer science and library and information science as sublanguage sources. Evaluates the results using three criteria. Discuss the practical and theoretical significance of this research and plans for further experiments

0.008738637 = product of:
  0.05243182 = sum of:
    0.05243182 = weight(_text_:computer in 36) [ClassicSimilarity], result of:
      0.05243182 = score(doc=36,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.32301605 = fieldWeight in 36, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=36)
  0.16666667 = coord(1/6)

Abstract

Analyzed a special type of man-mchine interaction, that of searching an information system with natural language. A model for full text processing for information retrieval was proposed that considered the system's users and how they employ information. Describes how INIST (the National Institute for Scientific and Technical Information) is developing computer assisted indexing as an aid to improving relevance when retrieving information from bibliographic data banks

0.008738637 = product of:
  0.05243182 = sum of:
    0.05243182 = weight(_text_:computer in 1911) [ClassicSimilarity], result of:
      0.05243182 = score(doc=1911,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.32301605 = fieldWeight in 1911, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=1911)
  0.16666667 = coord(1/6)

Abstract

Discusses research into the application of adaptive pattern recognition technology to enable effective retrieval from scanned document images. Describes application at the British Library of Excalibur EFS software which uses adaptive pattern recognition technology to provide access to digital information in its native forms, fuzzy searching retrieval and automatic indexing capabilities. It was used to make specialist printed catalogues and indexes accessible on computer via content based indexes

0.008738637 = product of:
  0.05243182 = sum of:
    0.05243182 = weight(_text_:computer in 7414) [ClassicSimilarity], result of:
      0.05243182 = score(doc=7414,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.32301605 = fieldWeight in 7414, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=7414)
  0.16666667 = coord(1/6)

Abstract

Research in the Knowledge-Based Systems Group of the University of Twente in the Netherlands is aimed at reducing information overload. One approach is to support indexing by the traditional method of assigning content descriptions to find documents. A second way is to use a computer program to determine what the document says without descriptors. Discusses automated indexing and direct access to information

0.008738637 = product of:
  0.05243182 = sum of:
    0.05243182 = weight(_text_:computer in 151) [ClassicSimilarity], result of:
      0.05243182 = score(doc=151,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.32301605 = fieldWeight in 151, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0625 = fieldNorm(doc=151)
  0.16666667 = coord(1/6)

Abstract

In 1993 the British Library established a programme of activities entitled Initiatives for Access (IFA) to identify and develop computer applications based on the new technologies emerging in the aereas of digital and network service. Discusses the problem of the effective retrieval of digital data after its capture focusing on the product Excalibur EFS which looks at the way information is sorted at its fundamental level and identifies patterns in numbers. Looks at the benefits of Excalibur and outlines other experiments in progress as part of the IFA programme

0.008710952 = product of:
  0.052265707 = sum of:
    0.052265707 = weight(_text_:web in 2533) [ClassicSimilarity], result of:
      0.052265707 = score(doc=2533,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.36057037 = fieldWeight in 2533, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.078125 = fieldNorm(doc=2533)
  0.16666667 = coord(1/6)

0.008623403 = product of:
  0.05174041 = sum of:
    0.05174041 = weight(_text_:web in 6750) [ClassicSimilarity], result of:
      0.05174041 = score(doc=6750,freq=4.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.35694647 = fieldWeight in 6750, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6750)
  0.16666667 = coord(1/6)

Abstract

As the amount of accessible information on the WWW increases, so will the cost of accessing it, even if search servcies remain free, due to the increasing amount of time users will have to spend to find needed items. Considers what the seemingly unorganized Web and the organized world of libraries can offer each other. The OCLC Scorpion Project is attempting to combine indexing and cataloguing, specifically focusing on building tools for automatic subject recognition using the technqiues of library science and information retrieval. If subject headings or concept domains can be automatically assigned to electronic items, improved filtering tools for searching can be produced

0.008245593 = product of:
  0.049473554 = sum of:
    0.049473554 = product of:
      0.09894711 = sum of:
        0.09894711 = weight(_text_:programs in 8524) [ClassicSimilarity], result of:
          0.09894711 = score(doc=8524,freq=2.0), product of:
            0.25748047 = queryWeight, product of:
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.044416238 = queryNorm
            0.38428974 = fieldWeight in 8524, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.046875 = fieldNorm(doc=8524)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)

Abstract

Examines how techniques in the field of natural language processing can be applied to the analysis of text in information retrieval. State of the art text searching programs cannot distinguish, for example, between occurrences of the sickness, AIDS and aids as tool or between library school and school nor equate such terms as online or on-line which are variants of the same form. To make these distinction, systems must incorporate knowledge about the meaning of words in context. Research in natural language processing has concentrated on the automatic 'understanding' of language; how to analyze the grammatical structure and meaning of text. Although many asoects of this research remain experimental, describes how these techniques to recognize spelling variants, names, acronyms, and abbreviations