Summary of the paper

Title Automatic Discovery of Semantic Relations using MindNet
Authors Zareen Syed, Evelyne Viegas and Savas Parastatidis
Abstract Information extraction deals with extracting entities (such as people, organizations or locations) and named relations between entities (such as ""People born-in Country"") from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new “model” to extract semantic relations fully automatically from text using the Encarta encyclopedia and lexical-semantic relations discovered by MindNet. MindNet is a lexical knowledge base that can be constructed fully automatically from a given text corpus without any human intervention. Encarta articles are categorized and linked to related articles by experts. We demonstrate how the structured data available in Encarta and the lexical semantic relations between words in MindNet can be used to enrich MindNet with semantic relations between entities. With a slight trade off of accuracy a semantically enriched MindNet can be used to extract relations from a text corpus without any human intervention.
Topics Information Extraction, Information Retrieval, Semantics, Other
Full paper Automatic Discovery of Semantic Relations using MindNet
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Bibtex @InProceedings{SYED10.78,
  author = {Zareen Syed and Evelyne Viegas and Savas Parastatidis},
  title = {Automatic Discovery of Semantic Relations using MindNet},
  booktitle = {Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)},
  year = {2010},
  month = {may},
  date = {19-21},
  address = {Valletta, Malta},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis and Mike Rosner and Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-6-7},
  language = {english}
 }
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