Search (2 results, page 1 of 1)

  • × language_ss:"chi"
  • × theme_ss:"Computerlinguistik"
  1. Xianghao, G.; Yixin, Z.; Li, Y.: ¬A new method of news test understanding and abstracting based on speech acts theory (1998) 0.00
    0.00395732 = product of:
      0.01187196 = sum of:
        0.01187196 = weight(_text_:a in 3532) [ClassicSimilarity], result of:
          0.01187196 = score(doc=3532,freq=10.0), product of:
            0.05209492 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.045180224 = queryNorm
            0.22789092 = fieldWeight in 3532, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0625 = fieldNorm(doc=3532)
      0.33333334 = coord(1/3)
    
    Abstract
    Presents a method for the automated analysis and comprehension of foreign affairs news produced by a Chinese news agency. Notes that the development of the method was prededed by a study of the structuring rules of the news. Describes how an abstract of the news story is produced automatically from the analysis. Stresses the main aim of the work which is to use specch act theory to analyse and classify sentences
    Type
    a
  2. Cheng, K.-H.: Automatic identification for topics of electronic documents (1997) 0.00
    0.002682161 = product of:
      0.008046483 = sum of:
        0.008046483 = weight(_text_:a in 1811) [ClassicSimilarity], result of:
          0.008046483 = score(doc=1811,freq=6.0), product of:
            0.05209492 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.045180224 = queryNorm
            0.1544581 = fieldWeight in 1811, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0546875 = fieldNorm(doc=1811)
      0.33333334 = coord(1/3)
    
    Abstract
    With the rapid rise in numbers of electronic documents on the Internet, how to effectively assign topics to documents become an important issue. Current research in this area focuses on the behaviour of nouns in documents. Proposes, however, that nouns and verbs together contribute to the process of topic identification. Constructs a mathematical model taking into account the following factors: word importance, word frequency, word co-occurence, and word distance. Preliminary experiments ahow that the performance of the proposed model is equivalent to that of a human being
    Type
    a