Search (1 results, page 1 of 1)

0.015005117 = product of:
  0.022507675 = sum of:
    0.010324181 = product of:
      0.041296724 = sum of:
        0.041296724 = weight(_text_:authors in 2509) [ClassicSimilarity], result of:
          0.041296724 = score(doc=2509,freq=2.0), product of:
            0.23425597 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.051385287 = queryNorm
            0.17628889 = fieldWeight in 2509, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.02734375 = fieldNorm(doc=2509)
      0.25 = coord(1/4)
    0.012183495 = product of:
      0.02436699 = sum of:
        0.02436699 = weight(_text_:22 in 2509) [ClassicSimilarity], result of:
          0.02436699 = score(doc=2509,freq=2.0), product of:
            0.17994252 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051385287 = queryNorm
            0.1354154 = fieldWeight in 2509, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.02734375 = fieldNorm(doc=2509)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

A relevancy-ranking algorithm for a natural language interface to Boolean online public access catalogs (OPACs) was formulated and compared with that currently used in a knowledge-based search interface called the E-Referencer, being developed by the authors. The algorithm makes use of seven weIl-known ranking criteria: breadth of match, section weighting, proximity of query words, variant word forms (stemming), document frequency, term frequency and document length. The algorithm converts a natural language query into a series of increasingly broader Boolean search statements. In a small experiment with ten subjects in which the algorithm was simulated by hand, the algorithm obtained good results with a mean overall precision of 0.42 and mean average precision of 0.62, representing a 27 percent improvement in precision and 41 percent improvement in average precision compared to the E-Referencer. The usefulness of each step in the algorithm was analyzed and suggestions are made for improving the algorithm.

Source

Electronic library. 22(2004) no.2, S.112-120