  This thesis addresses automatic lexical error recovery and tokenization of
corrupt text input. We propose a technique that can automatically correct
misspellings, segmentation errors and real-word errors in a unified framework
that uses both a model of language production and a model of the typing
behavior, and which makes tokenization part of the recovery process.
  The typing process is modeled as a noisy channel where Hidden Markov Models
are used to model the channel characteristics. Weak statistical language models
are used to predict what sentences are likely to be transmitted through the
channel. These components are held together in the Token Passing framework
which provides the desired tight coupling between orthographic pattern matching
and linguistic expectation.
  The system, CTR (Connected Text Recognition), has been tested on two corpora
derived from two different applications, a natural language dialogue system and
a transcription typing scenario. Experiments show that CTR can automatically
correct a considerable portion of the errors in the test sets without
introducing too much noise. The segmentation error correction rate is virtually
faultless.
