Search (3 results, page 1 of 1)

0.027950348 = product of:
  0.055900697 = sum of:
    0.055900697 = product of:
      0.11180139 = sum of:
        0.11180139 = weight(_text_:light in 716) [ClassicSimilarity], result of:
          0.11180139 = score(doc=716,freq=2.0), product of:
            0.2920221 = queryWeight, product of:
              5.7753086 = idf(docFreq=372, maxDocs=44218)
              0.050563898 = queryNorm
            0.3828525 = fieldWeight in 716, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.7753086 = idf(docFreq=372, maxDocs=44218)
              0.046875 = fieldNorm(doc=716)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

The objective of this paper is to highlight the importance of a scientifically sounded approach to search engine evaluation. Nowadays there is a flourishing literature which describes various attempts at conducting such evaluation by following all sort of approaches, but very often only the final results are published with little, if any, information about the methodology and the procedures adopted. These various experiments have been critically investigated and catalogued according to their scientific foundation by Bell [1] in the attempt to provide a valuable framework for future studies in this area. This paper reconsiders some of Bell's ideas in the light of the crisis of classic evaluation techniques for information retrieval and tries to envisage some form of collaboration between the IR and web communities in order to design a better and more consistent platform for the evaluation of tools for interactive information retrieval.

0.023291955 = product of:
  0.04658391 = sum of:
    0.04658391 = product of:
      0.09316782 = sum of:
        0.09316782 = weight(_text_:light in 5204) [ClassicSimilarity], result of:
          0.09316782 = score(doc=5204,freq=2.0), product of:
            0.2920221 = queryWeight, product of:
              5.7753086 = idf(docFreq=372, maxDocs=44218)
              0.050563898 = queryNorm
            0.31904373 = fieldWeight in 5204, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.7753086 = idf(docFreq=372, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5204)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Seven hundred natural language questions from TREC-8 and TREC-9 were sent by Radev, Libner, and Fan to each of nine web search engines. The top 40 sites returned by each system were stored for evaluation of their productivity of correct answers. Each question per engine was scored as the sum of the reciprocal ranks of identified correct answers. The large number of zero scores gave a positive skew violating the normality assumption for ANOVA, so values were transformed to zero for no hit and one for one or more hits. The non-zero values were then square-root transformed to remove the remaining positive skew. Interactions were observed between search engine and answer type (name, place, date, et cetera), search engine and number of proper nouns in the query, search engine and the need for time limitation, and search engine and total query words. All effects were significant. Shortest queries had the highest mean scores. One or more proper nouns present provides a significant advantage. Non-time dependent queries have an advantage. Place, name, person, and text description had mean scores between .85 and .9 with date at .81 and number at .59. There were significant differences in score by search engine. Search engines found at least one correct answer in between 87.7 and 75.45 of the cases. Google and Northern Light were just short of a 90% hit rate. No evidence indicated that a particular engine was better at answering any particular sort of question.

0.013701421 = product of:
  0.027402842 = sum of:
    0.027402842 = product of:
      0.054805685 = sum of:
        0.054805685 = weight(_text_:22 in 261) [ClassicSimilarity], result of:
          0.054805685 = score(doc=261,freq=2.0), product of:
            0.17706616 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050563898 = queryNorm
            0.30952093 = fieldWeight in 261, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0625 = fieldNorm(doc=261)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Die deutschen Suchmaschinen, Abacho, Acoon, Fireball und Lycos sowie die Web-Kataloge Web.de und Yahoo! werden einem Qualitätstest nach relativem Recall, Precision und Availability unterzogen. Die Methoden der Retrievaltests werden vorgestellt. Im Durchschnitt werden bei einem Cut-Off-Wert von 25 ein Recall von rund 22%, eine Precision von knapp 19% und eine Verfügbarkeit von 24% erreicht