  The study presented here relies on the integrated use of different kinds of
knowledge in order to improve first-guess accuracy in non-word
context-sensitive correction for general unrestricted texts. State of the art
spelling correction systems, e.g. ispell, apart from detecting spelling errors,
also assist the user by offering a set of candidate corrections that are close
to the misspelled word. Based on the correction proposals of ispell, we built
several guessers, which were combined in different ways. Firstly, we evaluated
all possibilities and selected the best ones in a corpus with artificially
generated typing errors. Secondly, the best combinations were tested on texts
with genuine spelling errors. The results for the latter suggest that we can
expect automatic non-word correction for all the errors in a free running text
with 80% precision and a single proposal 98% of the times (1.02 proposals on
average).
