Search (2 results, page 1 of 1)

  • × author_ss:"Melucci, M."
  • × theme_ss:"Computerlinguistik"
  1. Bacchin, M.; Ferro, N.; Melucci, M.: ¬A probabilistic model for stemmer generation (2005) 0.02 
    0.019919557 = product of:
  0.039839115 = sum of:
    0.016034503 = weight(_text_:information in 1001) [ClassicSimilarity], result of:
      0.016034503 = score(doc=1001,freq=4.0), product of:
        0.08351069 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047571484 = queryNorm
        0.1920054 = fieldWeight in 1001, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1001)
    0.023804612 = product of:
      0.047609225 = sum of:
        0.047609225 = weight(_text_:retrieval in 1001) [ClassicSimilarity], result of:
          0.047609225 = score(doc=1001,freq=4.0), product of:
            0.1438997 = queryWeight, product of:
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.047571484 = queryNorm
            0.33085006 = fieldWeight in 1001, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.024915 = idf(docFreq=5836, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1001)
      0.5 = coord(1/2)
  0.5 = coord(2/4)
    Abstract
    In this paper we will present a language-independent probabilistic model which can automatically generate stemmers. Stemmers can improve the retrieval effectiveness of information retrieval systems, however the designing and the implementation of stemmers requires a laborious amount of effort due to the fact that documents and queries are often written or spoken in several different languages. The probabilistic model proposed in this paper aims at the development of stemmers used for several languages. The proposed model describes the mutual reinforcement relationship between stems and derivations and then provides a probabilistic interpretation. A series of experiments shows that the stemmers generated by the probabilistic model are as effective as the ones based on linguistic knowledge.
    Source
    Information processing and management. 41(2005) no.1, S.121-137
  2. Melucci, M.; Orio, N.: Design, implementation, and evaluation of a methodology for automatic stemmer generation (2007) 0.00 
    0.0028345266 = product of:
  0.011338106 = sum of:
    0.011338106 = weight(_text_:information in 268) [ClassicSimilarity], result of:
      0.011338106 = score(doc=268,freq=2.0), product of:
        0.08351069 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.047571484 = queryNorm
        0.13576832 = fieldWeight in 268, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0546875 = fieldNorm(doc=268)
  0.25 = coord(1/4)
    Source
    Journal of the American Society for Information Science and Technology. 58(2007) no.5, S.673-686

Authors