Search (5 results, page 1 of 1)

0.0027415988 = product of:
  0.0054831975 = sum of:
    0.0054831975 = product of:
      0.010966395 = sum of:
        0.010966395 = weight(_text_:a in 3908) [ClassicSimilarity], result of:
          0.010966395 = score(doc=3908,freq=4.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.25222903 = fieldWeight in 3908, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.109375 = fieldNorm(doc=3908)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Type

a

0.002374294 = product of:
  0.004748588 = sum of:
    0.004748588 = product of:
      0.009497176 = sum of:
        0.009497176 = weight(_text_:a in 5907) [ClassicSimilarity], result of:
          0.009497176 = score(doc=5907,freq=12.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.21843673 = fieldWeight in 5907, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=5907)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Search key resolution power is analyzed in the context of a request, i.e., among the set of search keys for the request. Methods of characterizing the resolution power of keys automatically are studied, and the effects search keys of varying resolution power have on retrieval effectiveness are analyzed. It is shown that it often is possible to identify the best key of a query while the discrimination between the remaining keys presents problems. It is also shown that query performance is improved by suitably using the best key in a structured query. The tests were run with InQuery in a subcollection of the TREC collection, which contained some 515,000 documents

Type

a

0.002189429 = product of:
  0.004378858 = sum of:
    0.004378858 = product of:
      0.008757716 = sum of:
        0.008757716 = weight(_text_:a in 2771) [ClassicSimilarity], result of:
          0.008757716 = score(doc=2771,freq=20.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.20142901 = fieldWeight in 2771, product of:
              4.472136 = tf(freq=20.0), with freq of:
                20.0 = termFreq=20.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2771)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We present a novel measure for ranking evaluation, called Twist (t). It is a measure for informational intents, which handles both binary and graded relevance. t stems from the observation that searching is currently a that searching is currently taken for granted and it is natural for users to assume that search engines are available and work well. As a consequence, users may assume the utility they have in finding relevant documents, which is the focus of traditional measures, as granted. On the contrary, they may feel uneasy when the system returns nonrelevant documents because they are then forced to do additional work to get the desired information, and this causes avoidable effort. The latter is the focus of t, which evaluates the effectiveness of a system from the point of view of the effort required to the users to retrieve the desired information. We provide a formal definition of t, a demonstration of its properties, and introduce the notion of effort/gain plots, which complement traditional utility-based measures. By means of an extensive experimental evaluation, t is shown to grasp different aspects of system performances, to not require extensive and costly assessments, and to be a robust tool for detecting differences between systems.

Type

a

0.002035109 = product of:
  0.004070218 = sum of:
    0.004070218 = product of:
      0.008140436 = sum of:
        0.008140436 = weight(_text_:a in 1052) [ClassicSimilarity], result of:
          0.008140436 = score(doc=1052,freq=12.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.18723148 = fieldWeight in 1052, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1052)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Technical terms and proper names constitute a major problem in dictionary-based cross-language information retrieval (CLIR). However, technical terms and proper names in different languages often share the same Latin or Greek origin, being thus spelling variants of each other. In this paper we present a novel two-step fuzzy translation technique for cross-lingual spelling variants. In the first step, transformation rules are applied to source words to render them more similar to their target language equivalents. The rules are generated automatically using translation dictionaries as source data. In the second step, the intermediate forms obtained in the first step are translated into a target language using fuzzy matching. The effectiveness of the technique was evaluated empirically using five source languages and English as a target language. The two-step technique performed better, in some cases considerably better, than fuzzy matching alone. Even using the first step as such showed promising results.

Type

a

0.002035109 = product of:
  0.004070218 = sum of:
    0.004070218 = product of:
      0.008140436 = sum of:
        0.008140436 = weight(_text_:a in 1074) [ClassicSimilarity], result of:
          0.008140436 = score(doc=1074,freq=12.0), product of:
            0.043477926 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.037706986 = queryNorm
            0.18723148 = fieldWeight in 1074, product of:
              3.4641016 = tf(freq=12.0), with freq of:
                12.0 = termFreq=12.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1074)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

We will explore various ways to apply query structuring in cross-language information retrieval. In the first test, English queries were translated into Finnish using an electronic dictionary, and were run in a Finnish newspaper database of 55,000 articles. Queries were structured by combining the Finnish translation equivalents of the same English query key using the syn-operator of the InQuery retrieval system. Structured queries performed markedly better than unstructured queries. Second, the effects of compound-based structuring using a proximity operator for the translation equivalents of query language compound components were tested. The method was not useful in syn-based queries but resulted in decrease in retrieval effectiveness. Proper names are often non-identical spelling variants in different languages. This allows n-gram based translation of names not included in a dictionary. In the third test, a query structuring method where the Boolean and-operator was used to assign more weight to keys translated through n-gram matching gave good results.

Type

a