Search (14 results, page 1 of 1)

0.037970424 = product of:
  0.11391127 = sum of:
    0.11391127 = sum of:
      0.018938582 = weight(_text_:of in 337) [ClassicSimilarity], result of:
        0.018938582 = score(doc=337,freq=2.0), product of:
          0.06850986 = queryWeight, product of:
            1.5637573 = idf(docFreq=25162, maxDocs=44218)
            0.043811057 = queryNorm
          0.27643585 = fieldWeight in 337, 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=337)
      0.09497269 = weight(_text_:22 in 337) [ClassicSimilarity], result of:
        0.09497269 = score(doc=337,freq=2.0), product of:
          0.15341885 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.043811057 = queryNorm
          0.61904186 = fieldWeight in 337, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.125 = fieldNorm(doc=337)
  0.33333334 = coord(1/3)

Source

Annual review of information science and technology. 22(1987), S.79-108

0.019373938 = product of:
  0.058121815 = sum of:
    0.058121815 = sum of:
      0.01657126 = weight(_text_:of in 1361) [ClassicSimilarity], result of:
        0.01657126 = score(doc=1361,freq=8.0), product of:
          0.06850986 = queryWeight, product of:
            1.5637573 = idf(docFreq=25162, maxDocs=44218)
            0.043811057 = queryNorm
          0.24188137 = fieldWeight in 1361, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            1.5637573 = idf(docFreq=25162, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1361)
      0.041550554 = weight(_text_:22 in 1361) [ClassicSimilarity], result of:
        0.041550554 = score(doc=1361,freq=2.0), product of:
          0.15341885 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.043811057 = queryNorm
          0.2708308 = fieldWeight in 1361, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0546875 = fieldNorm(doc=1361)
  0.33333334 = coord(1/3)

Abstract

THESYS is based on the natural language processing of free-text databases. It yields statistically evaluated correlations between words of the database. These correlations correspond to traditional thesaurus relations. The person who has to build a thesaurus is thus assisted by the proposals made by THESYS. THESYS is being tested on commercial databases under real world conditions. It is part of a text processing project at Siemens, called TINA (Text-Inhalts-Analyse). Software from TINA is actually being applied and evaluated by the US Department of Commerce for patent search and indexing (REALIST: REtrieval Aids by Linguistics and STatistics)

Date

6. 1.1999 10:22:07

0.004184875 = product of:
  0.012554625 = sum of:
    0.012554625 = product of:
      0.02510925 = sum of:
        0.02510925 = weight(_text_:of in 1845) [ClassicSimilarity], result of:
          0.02510925 = score(doc=1845,freq=36.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.36650562 = fieldWeight in 1845, 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=1845)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

It may be possible to improve the quality of automatic indexing systems by using complex descriptors, for example, phrases, in addition to the simple descriptors (words or word stems) that are normally used in automatically constructed representations of document content. This study is directed toward the goal of developing effective methods of identifying phrases in natural language text from which good quality phrase descriptors can be constructed. The effectiveness of one method, a simple nonsyntactic phrase indexing procedure, has been tested on five experimental document collections. The results have been analyzed in order to identify the inadequacies of the procedure, and to determine what kinds of information about text structure are needed in order to construct phrase descriptors that are good indicators of document content. Two primary conclusions have been reached: (1) In the retrieval experiments, the nonsyntactic phrase construction procedure did not consistently yield substantial improvements in effectiveness. It is therefore not likely that phrase indexing of this kind will prove to be an important method of enhancing the performance of automatic document indexing and retrieval systems in operational environments. (2) Many of the shortcomings of the nonsyntactic approach can be overcome by incorporating syntactic information into the phrase construction process. However, a general syntactic analysis facility may be required, since many useful sources of phrases cannot be exploited if only a limited inventory of syntactic patterns can be recognized. Further research should be conducted into methods of incorporating automatic syntactic analysis into content analysis for document retrieval.

Source

Journal of the American Society for Information Science. 40(1989) no.2, S.115-132

0.0041003237 = product of:
  0.01230097 = sum of:
    0.01230097 = product of:
      0.02460194 = sum of:
        0.02460194 = weight(_text_:of in 4819) [ClassicSimilarity], result of:
          0.02460194 = score(doc=4819,freq=6.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.3591007 = fieldWeight in 4819, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.09375 = fieldNorm(doc=4819)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

PRECIS

Languages / Translation / Applications of computer systems

Subject

Languages / Translation / Applications of computer systems

0.003865822 = product of:
  0.011597466 = sum of:
    0.011597466 = product of:
      0.023194931 = sum of:
        0.023194931 = weight(_text_:of in 3650) [ClassicSimilarity], result of:
          0.023194931 = score(doc=3650,freq=48.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.33856338 = fieldWeight in 3650, product of:
              6.928203 = tf(freq=48.0), with freq of:
                48.0 = termFreq=48.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.03125 = fieldNorm(doc=3650)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The attempt to computerize a process, such as indexing, abstracting, classifying, or retrieving information, begins with an analysis of the process into its intellectual and nonintellectual components. That part of the process which is amenable to computerization is mechanical or algorithmic. What is not is intellectual or creative and requires human intervention. Gerard Salton has been an innovator, experimenter, and promoter in the area of mechanized information systems since the early 1960s. He has been particularly ingenious at analyzing the process of information retrieval into its algorithmic components. He received a doctorate in applied mathematics from Harvard University before moving to the computer science department at Cornell, where he developed a prototype automatic retrieval system called SMART. Working with this system he and his students contributed for over a decade to our theoretical understanding of the retrieval process. On a more practical level, they have contributed design criteria for operating retrieval systems. The following selection presents one of the early descriptions of the SMART system; it is valuable as it shows the direction automatic retrieval methods were to take beyond simple word-matching techniques. These include various word normalization techniques to improve recall, for instance, the separation of words into stems and affixes; the correlation and clustering, using statistical association measures, of related terms; and the identification, using a concept thesaurus, of synonymous, broader, narrower, and sibling terms. They include, as weIl, techniques, both linguistic and statistical, to deal with the thorny problem of how to automatically extract from texts index terms that consist of more than one word. They include weighting techniques and various documentrequest matching algorithms. Significant among the latter are those which produce a retrieval output of citations ranked in relevante order. During the 1970s, Salton and his students went an to further refine these various techniques, particularly the weighting and statistical association measures. Many of their early innovations seem commonplace today. Some of their later techniques are still ahead of their time and await technological developments for implementation. The particular focus of the selection that follows is an the evaluation of a particular component of the SMART system, a multilingual thesaurus. By mapping English language expressions and their German equivalents to a common concept number, the thesaurus permitted the automatic processing of German language documents against English language queries and vice versa. The results of the evaluation, as it turned out, were somewhat inconclusive. However, this SMART experiment suggested in a bold and optimistic way how one might proceed to answer such complex questions as What is meant by retrieval language compatability? How it is to be achieved, and how evaluated?

Footnote

Original in: Journal of the American Society for Information Science 21(1970) no.3, S.187-194.

Source

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

0.0031564306 = product of:
  0.009469291 = sum of:
    0.009469291 = product of:
      0.018938582 = sum of:
        0.018938582 = weight(_text_:of in 7512) [ClassicSimilarity], result of:
          0.018938582 = score(doc=7512,freq=2.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.27643585 = fieldWeight in 7512, 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=7512)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

0.0031564306 = product of:
  0.009469291 = sum of:
    0.009469291 = product of:
      0.018938582 = sum of:
        0.018938582 = weight(_text_:of in 2858) [ClassicSimilarity], result of:
          0.018938582 = score(doc=2858,freq=8.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.27643585 = fieldWeight in 2858, 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=2858)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Describes the architecture and functioning of the Constituent Object Parser. This system has been developed specially for text processing applications such as information retrieval, which can benefit from structural comparisons between elements of text such as a query and a potentially relevant abstract. Describes the general way in which this objective influenced the design of the system.

Source

Journal of the American Society for Information Science. 40(1989) no.6, S.398-423

0.0031564306 = product of:
  0.009469291 = sum of:
    0.009469291 = product of:
      0.018938582 = sum of:
        0.018938582 = weight(_text_:of in 287) [ClassicSimilarity], result of:
          0.018938582 = score(doc=287,freq=2.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.27643585 = fieldWeight in 287, 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=287)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Source

Annual review of information science and technology. 16(1981), S.113-138

0.0030878722 = product of:
  0.009263616 = sum of:
    0.009263616 = product of:
      0.018527232 = sum of:
        0.018527232 = weight(_text_:of in 3645) [ClassicSimilarity], result of:
          0.018527232 = score(doc=3645,freq=40.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = 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)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

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

0.0027899165 = product of:
  0.008369749 = sum of:
    0.008369749 = product of:
      0.016739499 = sum of:
        0.016739499 = weight(_text_:of in 2037) [ClassicSimilarity], result of:
          0.016739499 = score(doc=2037,freq=4.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.24433708 = fieldWeight in 2037, 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=2037)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The basic problem in freetext retrieval is that the retrieval language is not properly adapted to that of the author. Morphological segmentation, where words with the same root are grouped together in the inverted file, is a good eliminator of noise and information loss, providing high recall but low precision

0.0027899165 = product of:
  0.008369749 = sum of:
    0.008369749 = product of:
      0.016739499 = sum of:
        0.016739499 = weight(_text_:of in 3607) [ClassicSimilarity], result of:
          0.016739499 = score(doc=3607,freq=4.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.24433708 = fieldWeight in 3607, 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=3607)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

The constituent object parser is designed to improve the precision and recall performance of information retrieval by providing more powerful matching procedures. Describes the dependency tree representations and the relationship between the intended use of the parser and its design.

0.0022319334 = product of:
  0.0066958 = sum of:
    0.0066958 = product of:
      0.0133916 = sum of:
        0.0133916 = weight(_text_:of in 6683) [ClassicSimilarity], result of:
          0.0133916 = score(doc=6683,freq=4.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.19546966 = fieldWeight in 6683, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=6683)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Abstract

Storing and accessing texts in a conceptual format has a number of advantages over traditional document retrieval methods. A conceptual format facilitates natural language access to text information. It can support imprecise and inexact queries, conceptual information summarisation, and, ultimately, document translation. Describes 2 methods which have been implemented in a prototype intelligent information retrieval system calles SCISOR (System for Conceptual Information Summarisation, Organization and Retrieval). Describes the text processing, language acquisition, and summarisation components of SCISOR

0.001972769 = product of:
  0.0059183068 = sum of:
    0.0059183068 = product of:
      0.0118366135 = sum of:
        0.0118366135 = weight(_text_:of in 1954) [ClassicSimilarity], result of:
          0.0118366135 = score(doc=1954,freq=2.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.17277241 = fieldWeight in 1954, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.078125 = fieldNorm(doc=1954)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Source

Proceedings of the Second Conference on Applied Natural Language Processing

0.0015782153 = product of:
  0.0047346456 = sum of:
    0.0047346456 = product of:
      0.009469291 = sum of:
        0.009469291 = weight(_text_:of in 5711) [ClassicSimilarity], result of:
          0.009469291 = score(doc=5711,freq=2.0), product of:
            0.06850986 = queryWeight, product of:
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.043811057 = queryNorm
            0.13821793 = fieldWeight in 5711, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.5637573 = idf(docFreq=25162, maxDocs=44218)
              0.0625 = fieldNorm(doc=5711)
      0.5 = coord(1/2)
  0.33333334 = coord(1/3)

Source

Databases in the humanities 4. Proceedings of the International Conference on Databases in the Humanities and Social Sciences, Auburn University, Montgomery, Alabama, July 1987