Search (1 results, page 1 of 1)

0.0016417772 = product of:
  0.008208886 = sum of:
    0.008208886 = product of:
      0.024626656 = sum of:
        0.024626656 = weight(_text_:f in 3621) [ClassicSimilarity], result of:
          0.024626656 = score(doc=3621,freq=2.0), product of:
            0.11184496 = queryWeight, product of:
              3.985786 = idf(docFreq=2232, maxDocs=44218)
              0.028060954 = queryNorm
            0.22018565 = fieldWeight in 3621, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.985786 = idf(docFreq=2232, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3621)
      0.33333334 = coord(1/3)
  0.2 = coord(1/5)

Abstract

Natural language texts are valuable data sources in many human activities. NLP techniques are being widely used in order to help find the right information to specific needs. In this paper, we present one such technique: relation extraction from texts. This task aims at identifying and classifying semantic relations that occur between entities in a text. For example, the sentence "Roberto Marinho is the founder of Rede Globo" expresses a relation occurring between "Roberto Marinho" and "Rede Globo." This work presents a system for Portuguese Open Relation Extraction, named RelP, which extracts any relation descriptor that describes an explicit relation between named entities in the organisation domain by applying the Conditional Random Fields. For implementing RelP, we define the representation scheme, features based on previous work, and a reference corpus. RelP achieved state of the art results for open relation extraction; the F-measure rate was around 60% between the named entities person, organisation and place. For better understanding of the output, we present a way for organizing the output from the mining of the extracted relation descriptors. This organization can be useful to classify relation types, to cluster the entities involved in a common relation and to populate datasets.