Search (1 results, page 1 of 1)

  • × author_ss:"Sebastiani, F."
  • × theme_ss:"Automatisches Klassifizieren"
  1. Sebastiani, F.: Classification of text, automatic (2006) 0.01
    0.009529176 = product of:
      0.019058352 = sum of:
        0.019058352 = product of:
          0.038116705 = sum of:
            0.038116705 = weight(_text_:systems in 5003) [ClassicSimilarity], result of:
              0.038116705 = score(doc=5003,freq=2.0), product of:
                0.16037072 = queryWeight, product of:
                  3.0731742 = idf(docFreq=5561, maxDocs=44218)
                  0.052184064 = queryNorm
                0.23767869 = fieldWeight in 5003, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.0731742 = idf(docFreq=5561, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=5003)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Automatic text classification (ATC) is a discipline at the crossroads of information retrieval (IR), machine learning (ML), and computational linguistics (CL), and consists in the realization of text classifiers, i.e. software systems capable of assigning texts to one or more categories, or classes, from a predefined set. Applications range from the automated indexing of scientific articles, to e-mail routing, spam filtering, authorship attribution, and automated survey coding. This article will focus on the ML approach to ATC, whereby a software system (called the learner) automatically builds a classifier for the categories of interest by generalizing from a "training" set of pre-classified texts.