The modern study of child language development owes much to the methodological and conceptual advances introduced by Brown (1973). In his study of the language development of Adam, Eve, and Sarah, Roger Brown focused on a variety of core measurement issues, such as acquisition sequence, growth curves, morpheme

... a lexicon populated by units of various sizes, as envisaged by (Langacker, 1987). Constructions may be specified completely, as in the case of simple morphemes or idioms such as take it to the bank, or partially, as in the expression what’s X doing Y?, where X and Y are slots that admit fillers of particular types (Kay and Fillmore, 1999). Constructions offer an intriguing alternative to traditional rule-based syntax by hinting at the extent to which the complexity of language can stem from a rich repertoire of stored, more or less entrenched (Harris, 1998) representations that address both syntactic and semantic issues, and encompass, in addition to general rules, “totally idiosyncratic forms and patterns of all intermediate degrees of generality ” (Langacker, 1987, p.46). Because constructions are by their very nature language-specific, the question of acquisition in Construction Grammar is especially poignant. We address this issue by offering an unsupervised algorithm that learns constructions from raw corpora.

We outline an unsupervised language acquisition algorithm and offer some psycholinguistic support for a model based on it. Our approach resembles the Construction Grammar in its general philosophy, and the Tree Adjoining Grammar in its computational characteristics. The model is trained on a corpus of transcribed child-directed speech (CHILDES). The model's ability to process novel inputs makes it capable of taking various standard tests of English that rely on forced-choice judgment and on magnitude estimation of linguistic acceptability. We report encouraging results from several such tests, and discuss the limitations revealed by other tests in our present method of dealing with novel stimuli.

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).