In this article we show how Optimality Theory yields a highly general Constraint Demotion principle for grammar learning. The resulting learning procedure specifically exploits the grammatical structure of Optimality Theory, independent of the content of substantive constraints defining any given grammatical module. We decompose the learning problem and present formal results for a central subproblem, deducing the constraint ranking particular to a target language, given structural descriptions of positive examples. The structure imposed on the space of possible grammars by Optimality Theory allows efficient convergence to a correct grammar. We discuss implications for learning from overt data only, as well as other learning issues. We argue that Optimality Theory promotes confluence of the demands of more effective learnability and deeper linguistic explanation.

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This paper presents a computational model of language learning, transmission, and evolution. We contrast two explanations for the observed fit of language universals with language function that are prominent in the linguistics literature, and which appear to rely on very different explanatory mechanisms --- innate constraints on the one hand, and parsing influenced language change on the other. We show using our model that both explanations can be subsumed under one mechanism of differential take up of competing forms in the language community and subsequent evolution of the learning mechanism to efficiently learn regularities in the input. 1 Introduction One of the interesting challenges facing linguistics today is the explanation of the observed constraints on crosslinguistic variation. 1 Traditional linguistic typology (e.g. [14, 9, 16, 10]) as well as generative theories of language acquisition (e.g. [5, 15]) highlight the fact that the languages of the world appear to fall int...

An account of grammatical acquisition is developed within the parametersetting framework applied to a generalized categorial grammar (GCG). The GCG is embedded in a default inheritance network yielding a natural partial ordering (reflecting generality) of parameters which determines a partial order for parameter setting. Computational simulation shows that several resulting acquisition procedures are effective on a parameter set expressing major typological distinctions based on constituent order, and defining 70 distinct full languages and over 200 subset languages. The effects on acquisition of inductive bias, that is, of differing initial parameter settings, are explored via computational simulation. Computational simulation of populations of language learners and users instantiating the acquisition model show: 1) that variant acquisition procedures, with differing inductive biases, exert differing selective pressures on the evolution of language(s); 2) acquisition proc...

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The grammars of natural languages may be learned by using genetic algorithms that reproduce and mutate grammatical rules and part-of-speech tags, improving the quality of later generations of grammatical components. Syntactic rules are randomly generated and then evolve; those rules resulting in improved parsing and occasionally improved retrieval and filtering performance are allowed to further propagate. The LUST system learns the characteristics of the language or sublanguage used in document abstracts by learning from the document rankings obtained from the parsed abstracts. Unlike the application of traditional linguistic rules to retrieval and filtering applications, LUST develops grammatical structures and tags without the prior imposition of some common grammatical assumptions (e.g., part-of-speech assumptions), producing grammars that are empirically based and are optimized for this particular application. The author wishes to thank Stephanie Haas for discussions...

this paper was substantially helped by a UK ESRC research grant, No. R000 237551. I thank Simon Kirby, Ted Briscoe and Mike Oliphant for helpful comments, but I take sole responsibility for what is said here.

This paper provides a tutorial introduction to computational studies of how children learn their native languages. Its aim is to make recent advances accessible to the broader research community. and to place them in the context of current theoretical issues. The first section locates computational studies and behavioral studies within a common theoretical framework. The next two sections review two papers that appear in this volume: one on learning the meanings of words and one on learning the sounds of words. The following section highlights an idea which emerges independently in these two papers and which I have dubbed autonomous bootstrapping. Classical bootstrapping hypotheses propose that children begin to get a toe-hold in a particular linguistic domain, such as syntax, by exploiting information from another domain, such as semantics. Autonomous bootstrapping complements the cross-domain acquisition strategies of classical bootstrapping with strategies that apply within a single domain. Autonomous bootstrapping strategies work by representing partial and/or uncertain linguistic knowledge and using it to analyze the input. The next two sections review two more more contributions to this special issue: one on learning word meanings via selectional preferences and one on algorithms for setting grammatical parameters. The final section suggests directions for future research.

This paper considers the problem of language change. Linguists must explain not only how languages are learned but also how and why they have evolved along certain trajectories and not others. While the language learning problem has focused on the behavior of individuals and how they acquire a particular grammar from a class of grammars G, here we consider a population of such learners and investigate the emergent, global population characteristics of linguistic communities over several generations. We argue that language change follows logically from specific assumptions about grammatical theories and learning paradigms. In particular, we are able to transform parameterized theories and memoryless acquisition algorithms into grammatical dynamical systems, whose evolution depicts a population's evolving linguistic composition. We investigate the linguistic and computational consequences of this model, showing that the formalization allows one to ask questions about diachronic that one ...