@inbook{Inkpen9, 
   author = "Diana Inkpen and Graeme Hirst",
   chapter = "Experiments on extracting knowledge from a machine-readable dictionary of synonym differences",
   note = "Published as Lecture Notes in Computer Science, vol 2004",
   editor = "Gelbukh, Alexander",
   title = "Computational Linguistics and Intelligent Text Processing (Proceedings, Second Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, February 2001)",
   year = "2001",
   address = "Berlin",
   publisher = "Springer-Verlag",
   pages = "264--278",
   abstract = "In machine translation and natural language generation, making the
              wrong word choice from a set of near-synonyms can be imprecise or
              awkward, or convey unwanted implications.  Using Edmonds's model of
              lexical knowledge to represent clusters of near-synonyms, our goal is
              to automatically derive a lexical knowledge-base from the <I>Choose
              the Right Word</I> dictionary of near-synonym discrimination.  We do
              this by automatically classifying sentences in this dictionary
              according to the classes of distinctions they express.  We use a
              decision-list learning algorithm to learn words and expressions that
              characterize the classes DENOTATIONAL DISTINCTIONS and ATTITUDE-STYLE
              DISTINCTIONS.  These results are then used by an extraction module to
              actually extract knowledge from each sentence.  We also integrate a
              module to resolve anaphors and word-to-word comparisons.  We evaluate
              the results of our algorithm for several randomly selected clusters
              against a manually built standard solution, and compare them with the
              results of a baseline algorithm.",
  download = "http://ftp.cs.toronto.edu/pub/gh/Inkpen+Hirst-2001.pdf"
}