Summary of the paper

Title A Large List of Confusion Sets for Spellchecking Assessed Against a Corpus of Real-word Errors
Authors Jennifer Pedler and Roger Mitton
Abstract One of the methods that has been proposed for dealing with real-word errors (errors that occur when a correctly spelled word is substituted for the one intended) is the ""confusion-set"" approach - a confusion set being a small group of words that are likely to be confused with one another. Using a list of confusion sets drawn up in advance, a spellchecker, on finding one of these words in a text, can assess whether one of the other members of its set would be a better fit and, if it appears to be so, propose that word as a correction. Much of the research using this approach has suffered from two weaknesses. The first is the small number of confusion sets used. The second is that systems have largely been tested on artificial errors. In this paper we address these two weaknesses. We describe the creation of a realistically sized list of confusion sets, then the assembling of a corpus of real-word errors, and then we assess the potential of that list in relation to that corpus.
Topics Corpus (creation, annotation, etc.), Grammar and Syntax, Tools, systems, applications
Full paper A Large List of Confusion Sets for Spellchecking Assessed Against a Corpus of Real-word Errors
Slides A Large List of Confusion Sets for Spellchecking Assessed Against a Corpus of Real-word Errors
Bibtex @InProceedings{PEDLER10.122,
  author = {Jennifer Pedler and Roger Mitton},
  title = {A Large List of Confusion Sets for Spellchecking Assessed Against a Corpus of Real-word Errors},
  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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