Search (2 results, page 1 of 1)

0.0015690941 = product of:
  0.01882913 = sum of:
    0.01882913 = weight(_text_:internet in 4043) [ClassicSimilarity], result of:
      0.01882913 = score(doc=4043,freq=2.0), product of:
        0.09621047 = queryWeight, product of:
          2.9522398 = idf(docFreq=6276, maxDocs=44218)
          0.032588977 = queryNorm
        0.1957077 = fieldWeight in 4043, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.9522398 = idf(docFreq=6276, maxDocs=44218)
          0.046875 = fieldNorm(doc=4043)
  0.083333336 = coord(1/12)

Abstract

This paper presents a clustered approach to designing an overall ontological model together with a general rule-based component that serves as a mapping device. By observational criteria, a multi-lingual team of experts excerpts concepts from general communication in the media. The team, then, finds equivalent expressions in English, German, French, and Spanish. On the basis of a set of ontological and lexical relations, a conceptual network is built up. Concepts are thought to be universal. Objects unique in time and space are identified by names and will be explained by the universals as their instances. Our approach relies on multi-relational descriptions of concepts. It provides a powerful tool for documentation and conceptual language learning. First and foremost, our multi-lingual, polyhierarchical ontology fills the gap of semantically-based information retrieval by generating enhanced and improved queries for internet search

9.1530493E-4 = product of:
  0.010983659 = sum of:
    0.010983659 = weight(_text_:internet in 1164) [ClassicSimilarity], result of:
      0.010983659 = score(doc=1164,freq=2.0), product of:
        0.09621047 = queryWeight, product of:
          2.9522398 = idf(docFreq=6276, maxDocs=44218)
          0.032588977 = queryNorm
        0.11416282 = fieldWeight in 1164, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.9522398 = idf(docFreq=6276, maxDocs=44218)
          0.02734375 = fieldNorm(doc=1164)
  0.083333336 = coord(1/12)

Abstract

The explosive growth of the Internet and other sources of networked information have made automatic mediation of access to networked information sources an increasingly important problem. Much of this information is expressed as electronic text, and it is becoming practical to automatically convert some printed documents and recorded speech to electronic text as well. Thus, automated systems capable of detecting useful documents are finding widespread application. With even a small number of languages it can be inconvenient to issue the same query repeatedly in every language, so users who are able to read more than one language will likely prefer a multilingual text retrieval system over a collection of monolingual systems. And since reading ability in a language does not always imply fluent writing ability in that language, such users will likely find cross-language text retrieval particularly useful for languages in which they are less confident of their ability to express their information needs effectively. The use of such systems can be also be beneficial if the user is able to read only a single language. For example, when only a small portion of the document collection will ever be examined by the user, performing retrieval before translation can be significantly more economical than performing translation before retrieval. So when the application is sufficiently important to justify the time and effort required for translation, those costs can be minimized if an effective cross-language text retrieval system is available. Even when translation is not available, there are circumstances in which cross-language text retrieval could be useful to a monolingual user. For example, a researcher might find a paper published in an unfamiliar language useful if that paper contains references to works by the same author that are in the researcher's native language.