Linguistic Structure as Composition and Perturbation
  This paper discusses the problem of learning language from unprocessed text and speech signals, concentrating on the problem of learning a lexicon. In particular, it argues for a representation of language in which linguistic parameters like words are built by perturbing a composition of existing parameters. The power of this representation is demonstrated by several examples in text segmentation and compression, acquisition of a lexicon from raw speech, and the acquisition of mappings between text and artificial representations of meaning. 
Carl de Marcken (MIT Artificial Intelligence Lab.)