Search (2 results, page 1 of 1)

  • × author_ss:"Khoo, C."
  • × theme_ss:"Begriffstheorie"
  1. Khoo, C.; Chan, S.; Niu, Y.: ¬The many facets of the cause-effect relation (2002) 0.00 
    0.001823924 = product of:
  0.003647848 = sum of:
    0.003647848 = product of:
      0.007295696 = sum of:
        0.007295696 = weight(_text_:a in 1192) [ClassicSimilarity], result of:
          0.007295696 = score(doc=1192,freq=8.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.15287387 = fieldWeight in 1192, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1192)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    This chapter presents a broad survey of the cause-effect relation, with particular emphasis an how the relation is expressed in text. Philosophers have been grappling with the concept of causation for centuries. Researchers in social psychology have found that the human mind has a very complex mechanism for identifying and attributing the cause for an event. Inferring cause-effect relations between events and statements has also been found to be an important part of reading and text comprehension, especially for narrative text. Though many of the cause-effect relations in text are implied and have to be inferred by the reader, there is also a wide variety of linguistic expressions for explicitly indicating cause and effect. In addition, it has been found that certain words have "causal valence"-they bias the reader to attribute cause in certain ways. Cause-effect relations can also be divided into several different types.
    Type
    a
  2. Khoo, C.; Myaeng, S.H.: Identifying semantic relations in text for information retrieval and information extraction (2002) 0.00 
    0.0015795645 = product of:
  0.003159129 = sum of:
    0.003159129 = product of:
      0.006318258 = sum of:
        0.006318258 = weight(_text_:a in 1197) [ClassicSimilarity], result of:
          0.006318258 = score(doc=1197,freq=6.0), product of:
            0.04772363 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.041389145 = queryNorm
            0.13239266 = fieldWeight in 1197, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046875 = fieldNorm(doc=1197)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    Automatic identification of semantic relations in text is a difficult problem, but is important for many applications. It has been used for relation matching in information retrieval to retrieve documents that contain not only the concepts but also the relations between concepts specified in the user's query. It is an integral part of information extraction-extracting from natural language text, facts or pieces of information related to a particular event or topic. Other potential applications are in the construction of relational thesauri (semantic networks of related concepts) and other kinds of knowledge bases, and in natural language processing applications such as machine translation and computer comprehension of text. This chapter examines the main methods used for identifying semantic relations automatically and their application in information retrieval and information extraction.
    Type
    a

Authors