This article provides an introduction to the theory of automata and formal languages. The elements are presented in a historical perspective and the links with other areas are underlined. In particular, applications of the field to linguistics, software design, text processing, computational algebra or computational biology are given.

Abstract. Converging findings from English, Mandarin, and other languages suggest that observed “universals ” may be algorithmic. First, computational principles behind recently developed algorithms that acquire productive constructions from raw texts or transcribed child-directed speech impose family resemblance on learnable languages. Second, child-directed speech is particularly rich in statistical (and social) cues that facilitate learning of certain types of structures. Having surveyed a wide range of posited universals and found them wanting, Evans and Levinson (E&L) propose instead that the “common patterns ” observed in the organization of human languages are due to cognitive constraints and cultural factors. We offer empirical evidence in support of both these ideas. One kind of common pattern is readily apparent in the six examples of child-directed speech in Figure 1, in each of which partial matches between successive utterances serve to highlight the structural regularities of the underlying language. Two universal principles that allow such regularities to be learned can be traced to the work of Zellig Harris (1946; 1991). First, the discovery of language structure, from morphemes to phrases, can proceed by cross-utterance alignment and comparison (Harris, 1946; Edelman and Waterfall, 2007). Second, the fundamental task in describing a language is to state the departures from equiprobability in its sound- and word-sequences (Harris, 1991, p.32; cf. Goldsmith, 2007).