  This paper presents a model-based, unsupervised algorithm for recovering word
boundaries in a natural-language text from which they have been deleted. The
algorithm is derived from a probability model of the source that generated the
text. The fundamental structure of the model is specified abstractly so that
the detailed component models of phonology, word-order, and word frequency can
be replaced in a modular fashion. The model yields a language-independent,
prior probability distribution on all possible sequences of all possible words
over a given alphabet, based on the assumption that the input was generated by
concatenating words from a fixed but unknown lexicon. The model is unusual in
that it treats the generation of a complete corpus, regardless of length, as a
single event in the probability space. Accordingly, the algorithm does not
estimate a probability distribution on words; instead, it attempts to calculate
the prior probabilities of various word sequences that could underlie the
observed text. Experiments on phonemic transcripts of spontaneous speech by
parents to young children suggest that this algorithm is more effective than
other proposed algorithms, at least when utterance boundaries are given and the
text includes a substantial number of short utterances.
  Keywords: Bayesian grammar induction, probability models, minimum description
length (MDL), unsupervised learning, cognitive modeling, language acquisition,
segmentation
