Abstract not found

Previous research has shown that, for learners to fully acquire a miniature phrase structure language, the language must contain cues to the phrases—for example, prosodic grouping or morphological agreement of the words within a phrase (Morgan, Meier, & Newport, 1987, 1989). Research on word segmentation has shown that learners can use transitional probabilities between syllables to segment speech into word-like units (Saffran, Aslin, & Newport, 1996). In the present research, we combine and extend these two sets of findings, asking whether learners can use transitional probabilities between words (or word classes) to segment sentences into phrases, and use this phrasal information to fully acquire the syntax of a miniature language. Adult subjects were exposed to sentences from a miniature language. A pattern in the transitional probabilities between words—high within phrases, low at phrase boundaries—was created by adding syntactic properties that are widespread in natural languages: optional phrases, repeated phrases, moved phrases, different-sized form classes, or all four properties combined. All conditions outperformed controls in learning the language. The best learning occurred with all properties combined, despite the fact that this language was the most complex. These data address the important question of how language learning is successful in the face of the massive complexity of natural languages. In our experiments, learning got better, not worse, when properly structured complexity was added to a language. The results also show that the same type of statistical computation useful in word segmentation might be used as well in learning syntax, suggesting that the range of statistics needed for acquiring various types of structure in natural languages might be suitably small. Correspondence should be addressed to Susan P. Thompson, Department of Psychology, 205

A method for deriving phrase structure categories from structured samples of a context-free language is presented. The learning algorithm is based on adaptation and competition, as well as error backpropagation in a continuous vector space. These connectionist-style techniques become applicable to grammars as the traditional grammar formalism is generalized to use vectors instead of symbols as category labels. More generally, it is argued that the conversion of symbolic formalisms to continuous representations is a promising way of combining the connectionist learning techniques with the structures and theoretical insights embodied in classical models.

dentified many constraints on the form and processing of human languages. By incorporating these constraints into a language learning system, it is possible to build a system that learns to translate (infers functions and grammars for machine translation) from an aligned bilingual corpus of sentences using understandable, symbolic linguistic principles and representations. This work focuses on one particular constraint, the Marker Hypothesis, which is shown to be powerful, understandable, and computationally accessible. This hypothesis has been incorporated into a family of systems that infer such transfer functions using standard multivariate optimization techniques. These systems have been tested on a variety of language pairs and corpora, demonstrating the language and corpus independence of this approach. Furthermore, the design iv principles are in theory independent of any particular inference technique or grammatical representation and reflect only the constraints of the Marke

To what extent is linguistic structure learnable from statistical information in the input? One set of cues which might assist in the discovery of hierarchical phrase structure given serially presented input are the dependencies, or predictive relationships, present within phrases. In order to determine whether adult learners can use this statistical information, subjects were exposed to artificial languages which either contained or violated the kinds of dependencies which characterize natural languages. The results suggest that adults possess learning mechanisms which detect and utilize statistical cues to phrase and hierarchical structure. A second experiment contrasted the acquisition of these linguistic systems with the same grammars implemented as non-linguistic input (sequences of non-linguistic sounds or shapes). These findings suggest that constraints on the mechanisms which highlight the statistical cues which are most characteristic of human languages are not specifically tailored for language learning.

Variation set structure — partial alignment of successive utterances in child-directed speech — has been shown to correlate with progress in the acquisition of syntax by children. The present study demonstrates that arranging a certain proportion of utterances in a training corpus in variation sets facilitates word segmentation and phrase structure learning in miniature artificial languages by adults. Our findings have implications for understanding the mechanisms of L1 acquisition by children, and for the development of more efficient algorithms for automatic language acquisition, as well as better methods for L2 instruction.

Running head: vocabulary learning

Abstract not found