Search (2 results, page 1 of 1)

  • × author_ss:"Ravana, S.D."
  • × theme_ss:"Retrievalstudien"
  1. Ravana, S.D.; Taheri, M.S.; Rajagopal, P.: Document-based approach to improve the accuracy of pairwise comparison in evaluating information retrieval systems (2015) 0.00
    0.0025760243 = product of:
      0.010304097 = sum of:
        0.010304097 = weight(_text_:information in 2587) [ClassicSimilarity], result of:
          0.010304097 = score(doc=2587,freq=6.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.16796975 = fieldWeight in 2587, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2587)
      0.25 = coord(1/4)
    
    Abstract
    Purpose The purpose of this paper is to propose a method to have more accurate results in comparing performance of the paired information retrieval (IR) systems with reference to the current method, which is based on the mean effectiveness scores of the systems across a set of identified topics/queries. Design/methodology/approach Based on the proposed approach, instead of the classic method of using a set of topic scores, the documents level scores are considered as the evaluation unit. These document scores are the defined document's weight, which play the role of the mean average precision (MAP) score of the systems as a significance test's statics. The experiments were conducted using the TREC 9 Web track collection. Findings The p-values generated through the two types of significance tests, namely the Student's t-test and Mann-Whitney show that by using the document level scores as an evaluation unit, the difference between IR systems is more significant compared with utilizing topic scores. Originality/value Utilizing a suitable test collection is a primary prerequisite for IR systems comparative evaluation. However, in addition to reusable test collections, having an accurate statistical testing is a necessity for these evaluations. The findings of this study will assist IR researchers to evaluate their retrieval systems and algorithms more accurately.
    Source
    Aslib journal of information management. 67(2015) no.4, S.408-421
  2. Rajagopal, P.; Ravana, S.D.; Koh, Y.S.; Balakrishnan, V.: Evaluating the effectiveness of information retrieval systems using effort-based relevance judgment (2019) 0.00
    0.0021033147 = product of:
      0.008413259 = sum of:
        0.008413259 = weight(_text_:information in 5287) [ClassicSimilarity], result of:
          0.008413259 = score(doc=5287,freq=4.0), product of:
            0.06134496 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.034944877 = queryNorm
            0.13714671 = fieldWeight in 5287, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5287)
      0.25 = coord(1/4)
    
    Source
    Aslib journal of information management. 71(2019) no.1, S.2-17