Spelling Correction Using Context

Spelling Correction Using Context* Mohammad All Elmi and Martha Evens Department of Computer Science, Illinois Institute of Technology 10 West 31 Street, Chicago, Illinois 60616 (csevens@minna.iit.edu) Abstract This paper describes a spelling correction system that functions as part of an intelligent tutor that car- ries on a natural language dialogue with its users. The process that searches the lexicon is adaptive as is the system filter, to speed up the process. The basis of our approach is the interaction between the parser and the spelling corrector. Alternative cor- rection targets are fed back to the parser, which does a series of syntactic and semantic checks, based on the dialogue context, the sentence con- text, and the phrase context. 1. Introduction This paper describes how context-dependent spell- ing correction is performed in a natural language dialogue system under control of the parser. Our spelling correction system is a functioning part of an intelligent tutoring system called Circsim-Tutor [Elmi, 94] designed to help medical students learn the language and the techniques for causal reason- ing necessary to solve problems in cardiovascular physiology. The users type in answers to questions and requests for information. In this kind of man-machine dialogue, spelling correction is essential. The input is full of errors. Most medical students have little experience with keyboards and they constantly invent novel abbre- viations. After typing a few characters of a long word, users often decide to quit. Apparently, the user types a few characters and decides that (s)he has given the reader enough of a hint, so we get 'spec' for 'specification.' The approach to spelling correction is necessarily different from that used in word processing or other authoring systems, which submit candidate corrections and ask the user to make a selection. Our system must make automatic corrections and make them rapidly since the sys- tem has only a few seconds to parse the student input, update the student model, plan the appropri- ate response, turn it into sentences, and display those sentences on the screen. Our medical sublanguage contains many long *This work was supported by the Cognitive Science Pro- gram, Office of Naval Research under Grant No. N00014-94- 1-0338, to Illinois Institute of Technology. The content does not reflect the position or policy of the government and no official endorsement should be inferred. phrases that are used in the correction process. Our filtering system is adaptive; it begins with a wide acceptance interval and tightens the filter as better candidates appear. Error weights are position-sen- sitive. The parser accepts several replacement can- didates for a misspelled string from the spelling corrector and selects the best by applying syntactic and semantic rules. The selection process is dynamic and context-dependent. We believe that our approach has significant potential applications to other types of man-machine dialogues, espe- cially speech-understanding systems. There are about 4,500 words in our lexicon. 2. Spelling Correction Method The first step in spelling correction is the detection of an error. There are two possibilities: 1. The misspelled word …

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