Summary of the paper

Title Combining Resources: Taxonomy Extraction from Multiple Dictionaries
Authors Rogelio Nazar and Maarten Janssen
Abstract The idea that dictionaries are a good source for (computational) information has been around for a long while, and the extraction of taxonomic information from them is something that has been attempted several times. However, such information extraction was typically based on the systematic analysis of the text of a single dictionary. In this paper, we demonstrate how it is possible to extract taxonomic information without any analysis of the specific text, by comparing the same lexical entry in a number of different dictionaries. Counting word frequencies in the dictionary entry for the same word in different dictionaries leads to a surprisingly good recovery of taxonomic information, without the need for any syntactic analysis of the entries in question nor any kind of language-specific treatment. As a case in point, we will show in this paper an experiment extracting hyperonymy relations from several Spanish dictionaries, measuring the effect that the different number of dictionaries have on the results.
Topics Knowledge Discovery/Representation, Lexicon, lexical database, Ontologies
Full paper Combining Resources: Taxonomy Extraction from Multiple Dictionaries
Slides Combining Resources: Taxonomy Extraction from Multiple Dictionaries
Bibtex @InProceedings{NAZAR10.469,
  author = {Rogelio Nazar and Maarten Janssen},
  title = {Combining Resources: Taxonomy Extraction from Multiple Dictionaries},
  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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