@techreport{Budanitsky3,
   author = "Alexander Budanitsky",
   title = "Lexical Semantic Relatedness and its Application in Natural Language Processing",
   institution = "Department of Computer Science, University of Toronto",
   month = "August",
   year = "1999",
   number = "CSRG-390",
   abstract = "A great variety of natural language processing tasks, from word sense
               disambiguation to text summarization to speech recognition, rely heavily on
               the ability to measure <I>semantic relatedness</I> or
               <I>distance</I> between words of a natural language. This report is
               a comprehensive study of recent computational methods of measuring
               lexical semantic relatedness. A survey of methods, as well as their
               applications, is presented, and the question of evaluation is
               addressed both theoretically and experimentally. Application to the
               specific task of intelligent spelling checking is discussed in
               detail: the design of a prototype system for the detection and
               correction of malapropisms (words that are similar in spelling or
               sound to, but quite different in meaning from, intended words) is
               described, and results of experiments on using various measures as
               plug-ins are considered. Suggestions for research directions in the
               areas of measuring semantic relatedness and intelligent spelling
               checking are offered.",
   download = "http://ftp.cs.toronto.edu/pub/gh/Budanitsky-99.pdf"
}