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Author Vargas-Vera, M. pdf  doi
  Title A Framework for Extraction of Relations from Text using Relational Learning and Similarity Measures Type
  Year 2015 Publication Journal Of Universal Computer Science Abbreviated Journal J. Univers. Comput. Sci.  
  Volume 21 Issue 11 Pages 1482-1495  
  Keywords Semantic Learning; Relational Learning; Similarity Measures; Semantic Web  
  Abstract Named entity recognition (NER) has been studied largely in the Information Extraction community as it is one step in the construction of an Information Extraction System. However, to extract only names without contextual information is not sufficient if we want to be able to describe facts encountered in documents, in particular, academic documents. Then, there is a need for extracting relations between entities. This task is accomplished using relational learning algorithms embedded in an Information Extraction framework. In particular, we have extended two relational learning frameworks RAPIER and FOIL. Our proposed extended frameworks are equipped with DSSim (short for Dempster-Shafer Similarity) our similarity service. Both extended frameworks were tested using an electronic newsletter consisting of news articles describing activities or events happening in an academic institution as our main application is on education.  
  Address [Vargas-Vera, Maria] Adolfo Ibanez Univ, Vinia Del Mar, Chile, Email:  
  Corporate Author Thesis  
  Publisher Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0948-695x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000368457300008 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 578  
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