Search (4 results, page 1 of 1)

  • × author_ss:"Robertson, S.E."
  • × theme_ss:"Semantisches Umfeld in Indexierung u. Retrieval"
  1. Robertson, S.E.; Walker, S.; Hancock-Beaulieu, M.M.: Large test collection experiments of an operational, interactive system : OKAPI at TREC (1995) 0.01 
    0.010779679 = product of:
  0.032339036 = sum of:
    0.032339036 = weight(_text_:on in 6964) [ClassicSimilarity], result of:
      0.032339036 = score(doc=6964,freq=6.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.29462588 = fieldWeight in 6964, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0546875 = fieldNorm(doc=6964)
  0.33333334 = coord(1/3)
    Abstract
    The Okapi system has been used in a series of experiments on the TREC collections, investiganting probabilistic methods, relevance feedback, and query expansion, and interaction issues. Some new probabilistic models have been developed, resulting in simple weigthing functions that take account of document length and within document and within query term frequency. All have been shown to be beneficial when based on large quantities of relevance data as in the routing task. Interaction issues are much more difficult to evaluate in the TREC framework, and no benefits have yet been demonstrated from feedback based on small numbers of 'relevant' items identified by intermediary searchers
  2. Robertson, S.E.: On relevance weight estimation and query expansion (1986) 0.01 
    0.008890929 = product of:
  0.026672786 = sum of:
    0.026672786 = weight(_text_:on in 3875) [ClassicSimilarity], result of:
      0.026672786 = score(doc=3875,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.24300331 = fieldWeight in 3875, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.078125 = fieldNorm(doc=3875)
  0.33333334 = coord(1/3)
  3. Robertson, S.E.: On term selection for query expansion (1990) 0.01 
    0.007112743 = product of:
  0.021338228 = sum of:
    0.021338228 = weight(_text_:on in 2650) [ClassicSimilarity], result of:
      0.021338228 = score(doc=2650,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.19440265 = fieldWeight in 2650, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.0625 = fieldNorm(doc=2650)
  0.33333334 = coord(1/3)
  4. Vechtomova, O.; Robertson, S.E.: ¬A domain-independent approach to finding related entities (2012) 0.01 
    0.0053345575 = product of:
  0.016003672 = sum of:
    0.016003672 = weight(_text_:on in 2733) [ClassicSimilarity], result of:
      0.016003672 = score(doc=2733,freq=2.0), product of:
        0.109763056 = queryWeight, product of:
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.04990557 = queryNorm
        0.14580199 = fieldWeight in 2733, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          2.199415 = idf(docFreq=13325, maxDocs=44218)
          0.046875 = fieldNorm(doc=2733)
  0.33333334 = coord(1/3)
    Abstract
    We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities.

Authors

Years