@inbook{ Hirst30,
  author    = {Graeme Hirst and Steven Small and Garrison Cottrell and Michael Tanenhaus},
  title     = {Lexical ambiguity resolution}, 
  abstract   = {<p> Any computer system for understanding natural language input (even in relatively weak senses of the word <i>understanding</i>) needs to be able to resolve lexical ambiguities. In this paper, I describe the lexical ambiguity resolution component of one such system. </p><p> The basic strategy used for disambiguation is ``do it the way people do.'' While cognitive modeling is not the primary goal of this work, it is often a good strategy in artificial intelligence to consider cognitive modeling anyway; finding out how people do something and trying to copy them is a good way to get a program to do the same thing. In developing the system below, I was strongly influenced by psycholinguistic research on lexical access and negative priming---in particular by the results of Swinney; Seidenberg, Tanenhaus, Leiman, and Bienkowski; and Reder. I will discuss the degree to which the system is a model of ambiguity resolution in people.</p>},
  note   = {2003 epilogue to this paper:  <a href=http://ftp.cs.toronto.edu/pub/gh/Hirst-88-Epilogue-2003.pdf>PDF</a>},
  pages   = {73--107},
  url   = {http://ftp.cs.toronto.edu/pub/gh/Hirst-88.pdf} ,
  year   = {1988}
}

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