Error-tolerant recognition enables the recognition of strings that deviate
mildly from any string in the regular set recognized by the underlying finite
state recognizer. Such recognition has applications in error-tolerant
morphological processing, spelling correction, and approximate string matching
in information retrieval. After a description of the concepts and algorithms
involved, we give examples from two applications: In the context of
morphological analysis, error-tolerant recognition allows misspelled input word
forms to be corrected, and morphologically analyzed concurrently. We present an
application of this to error-tolerant analysis of agglutinative morphology of
Turkish words. The algorithm can be applied to morphological analysis of any
language whose morphology is fully captured by a single (and possibly very
large) finite state transducer, regardless of the word formation processes and
morphographemic phenomena involved. In the context of spelling correction,
error-tolerant recognition can be used to enumerate correct candidate forms
from a given misspelled string within a certain edit distance. Again, it can be
applied to any language with a word list comprising all inflected forms, or
whose morphology is fully described by a finite state transducer. We present
experimental results for spelling correction for a number of languages. These
results indicate that such recognition works very efficiently for candidate
generation in spelling correction for many European languages such as English,
Dutch, French, German, Italian (and others) with very large word lists of root
and inflected forms (some containing well over 200,000 forms), generating all
candidate solutions within 10 to 45 milliseconds (with edit distance 1) on a
SparcStation 10/41. For spelling correction in Turkish, error-tolerantComment: Replaces 9504031. gzipped, uuencoded postscript file. To appear in
Computational Linguistics Volume 22 No:1, 1996, Also available as
ftp://ftp.cs.bilkent.edu.tr/pub/ko/clpaper9512.ps.
