Search (5 results, page 1 of 1)

0.010085807 = product of:
  0.020171614 = sum of:
    0.020171614 = product of:
      0.08068646 = sum of:
        0.08068646 = weight(_text_:authors in 4545) [ClassicSimilarity], result of:
          0.08068646 = score(doc=4545,freq=4.0), product of:
            0.22654721 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04969433 = queryNorm
            0.35615736 = fieldWeight in 4545, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4545)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

This article presents a novel user-oriented interface for generalised informetric analysis and demonstrates how informetric calculations can easily and declaratively be specified through advanced data modelling techniques. The interface is declarative and at a high level. Therefore it is easy to use, flexible and extensible. It enables end users to perform basic informetric ad hoc calculations easily and often with much less effort than in contemporary online retrieval systems. It also provides several fruitful generalisations of typical informetric measurements like impact factors. These are based on substituting traditional foci of analysis, for instance journals, by other object types, such as authors, organisations or countries. In the interface, bibliographic data are modelled as complex objects (non-first normal form relations) and terminological and citation networks involving transitive relationships are modelled as binary relations for deductive processing. The interface is flexible, because it makes it easy to switch focus between various object types for informetric calculations, e.g. from authors to institutions. Moreover, it is demonstrated that all informetric data can easily be broken down by criteria that foster advanced analysis, e.g. by years or content-bearing attributes. Such modelling allows flexible data aggregation along many dimensions. These salient features emerge from the query interface's general data restructuring and aggregation capabilities combined with transitive processing capabilities. The features are illustrated by means of sample queries and results in the article.

0.008558091 = product of:
  0.017116182 = sum of:
    0.017116182 = product of:
      0.068464726 = sum of:
        0.068464726 = weight(_text_:authors in 4922) [ClassicSimilarity], result of:
          0.068464726 = score(doc=4922,freq=2.0), product of:
            0.22654721 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04969433 = queryNorm
            0.30220953 = fieldWeight in 4922, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=4922)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

In this article the authors look at the prescriptions advocated by Web search textbooks in the light of a selection of empirical data of real Web information search processes. They use the strategy of disjointed incrementalism, which is a theoretical foundation from decision making, to focus an how people face complex problems, and claim that such problem solving can be compared to the tasks searchers perform when interacting with the Web. The findings suggest that textbooks an Web searching should take into account that searchers only tend to take a certain number of sources into consideration, that the searchers adjust their goals and objectives during searching, and that searchers reconsider the usefulness of sources at different stages of their work tasks as well as their search tasks.

0.008558091 = product of:
  0.017116182 = sum of:
    0.017116182 = product of:
      0.068464726 = sum of:
        0.068464726 = weight(_text_:authors in 1349) [ClassicSimilarity], result of:
          0.068464726 = score(doc=1349,freq=2.0), product of:
            0.22654721 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04969433 = queryNorm
            0.30220953 = fieldWeight in 1349, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=1349)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

In this article, the authors present evaluation results for transitive dictionary-based cross-language information retrieval (CLIR) using graded relevance assessments in a best match retrieval environment. A text database containing newspaper articles and a related set of 35 search topics were used in the tests. Source language topics (in English, German, and Swedish) were automatically translated into the target language (Finnish) via an intermediate (or pivot) language. Effectiveness of the transitively translated queries was compared to that of the directly translated and monolingual Finnish queries. Pseudo-relevance feedback (PRF) was also used to expand the original transitive target queries. Cross-language information retrieval performance was evaluated on three relevance thresholds: stringent, regular, and liberal. The transitive translations performed well achieving, on the average, 85-93% of the direct translation performance, and 66-72% of monolingual performance. Moreover, PRF was successful in raising the performance of transitive translation routes in absolute terms as well as in relation to monolingual and direct translation performance applying PRF.

0.00841612 = product of:
  0.01683224 = sum of:
    0.01683224 = product of:
      0.03366448 = sum of:
        0.03366448 = weight(_text_:22 in 1369) [ClassicSimilarity], result of:
          0.03366448 = score(doc=1369,freq=2.0), product of:
            0.17402108 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.04969433 = queryNorm
            0.19345059 = fieldWeight in 1369, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1369)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

9. 2.2008 17:22:42

0.0071317423 = product of:
  0.014263485 = sum of:
    0.014263485 = product of:
      0.05705394 = sum of:
        0.05705394 = weight(_text_:authors in 139) [ClassicSimilarity], result of:
          0.05705394 = score(doc=139,freq=2.0), product of:
            0.22654721 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.04969433 = queryNorm
            0.25184128 = fieldWeight in 139, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=139)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Information retrieval systems' ability to retrieve highly relevant documents has become more and more important in the age of extremely large collections, such as the World Wide Web (WWW). The authors' aim was to find out how corpus-based cross-language information retrieval (CLIR) manages in retrieving highly relevant documents. They created a Finnish-Swedish comparable corpus from two loosely related document collections and used it as a source of knowledge for query translation. Finnish test queries were translated into Swedish and run against a Swedish test collection. Graded relevance assessments were used in evaluating the results and three relevance criterion levels-liberal, regular, and stringent-were applied. The runs were also evaluated with generalized recall and precision, which weight the retrieved documents according to their relevance level. The performance of the Comparable Corpus Translation system (COCOT) was compared to that of a dictionarybased query translation program; the two translation methods were also combined. The results indicate that corpus-based CUR performs particularly well with highly relevant documents. In average precision, COCOT even matched the monolingual baseline on the highest relevance level. The performance of the different query translation methods was further analyzed by finding out reasons for poor rankings of highly relevant documents.