Search (2 results, page 1 of 1)

  • × author_ss:"Liddy, E.D."
  • × theme_ss:"Retrievalalgorithmen"
  1. Liddy, E.D.; Diamond, T.; McKenna, M.: DR-LINK in TIPSTER (2000) 0.00
    0.0023797948 = product of:
      0.014278769 = sum of:
        0.014278769 = weight(_text_:in in 3907) [ClassicSimilarity], result of:
          0.014278769 = score(doc=3907,freq=2.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.24046129 = fieldWeight in 3907, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.125 = fieldNorm(doc=3907)
      0.16666667 = coord(1/6)
    
  2. Liddy, E.D.: ¬An alternative representation for documents and queries (1993) 0.00
    0.0015457221 = product of:
      0.009274333 = sum of:
        0.009274333 = weight(_text_:in in 7813) [ClassicSimilarity], result of:
          0.009274333 = score(doc=7813,freq=6.0), product of:
            0.059380736 = queryWeight, product of:
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.043654136 = queryNorm
            0.1561842 = fieldWeight in 7813, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.3602545 = idf(docFreq=30841, maxDocs=44218)
              0.046875 = fieldNorm(doc=7813)
      0.16666667 = coord(1/6)
    
    Abstract
    Describes an alternative method of representation for documents and queries in information retrieval systems to the 2 most common methods: free text, natural language representation and controlled language representation. The alternative method combines the advantage of both traditional approaches and overcomes the difficulties associated with each. The scheme was developed for use with Longman's Dictionary of Contemporary English and uses a computerized version of the dictionary for the automatic generation of summary level semantic representations of each document and query. The system tags each word in a document with the appropriate Subject Field Code (SFC) from the dictionary and the SFCs are summed and normalized to produce a weighted, fixed length vector of the SFC. The search system matches the query SFC vector to the document SFC vectors in the database. The documents are then ranked on the basis of their vector's similarity to the query