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...

The purpose of this work is to investigate the process of grammatical acquisition from data. In order to do that, a computational learning system is used, composed of a Universal Grammar with associated parameters, and a learning algorithm, following the Principles and Parameters Theory. The Universal Grammar is implemented as a Unification-Based Generalised Categorial Grammar, embedded in a default inheritance network of lexical types. The learning algorithm receives input from a corpus of spontaneous child-directed transcribed speech annotated with logical forms and sets the parameters based on this input. This framework is used as a basis to investigate several aspects of language acquisition. In this thesis I concentrate on the acquisition of subcategorisation frames and word order information, from data. The data to which the learner is exposed can be noisy and ambiguous, and I investigate how these factors a#ect the learning process. The results obtained show a robust learner converging towards the target grammar given the input data available. They also show how the amount of noise present in the input data a#ects the speed of convergence of the learner towards the target grammar. Future work is suggested for investigating the developmental stages of language acquisition as predicted by the learning model, with a thorough comparison with the developmental stages of a child. This is primarily a cognitive computational model of language learning that can be used to investigate and gain a better understanding of human language acquisition, and can potentially be relevant to the development of more adaptive NLP technology.

The main purpose of this article is to argue the merits of ‘population thinking’ in gaining insight into linguistic and, in particular, syntactic change. Population-level thinking and modelling can shed new light on many issues in the study of language acquisition and language change, and leads directly to a precise and useful characterisation of E-language. Something which is lacking in current generative linguistics. Moreover, this way of thinking is fully compatible with the major insights of the latter, and integrates them into a framework in which language variation and change are inherent and inevitable, rather than peripheral and/or accidental, properties of language. I will argue that (E-)languages are best modelled as particular kinds of dynamical systems; namely, complex adaptive systems (where these terms are used in technical senses made precise below). The article both introduces some relevant ideas and techniques from modern evolutionary theory, and from the mathematical and computational study of dynamical systems, and also offers a critique and review of some recent work on syntactic change in this emerging framework, arguing that a useful population model needs to support overlapping generations of language users and learners and to allow quite detailed modelling of differing demographic scenarios. I utilise simple linguistic scenarios based on constituent order changes to illustrate the ideas and techniques clearly. I abstract away from the sociolinguistic detail of the actuation

This paper is part of an ongoing research effort (Briscoe, 1997, 1998, 1999a,b,c,d) to develop a formal model of language acquisition, demonstrate that an innate language acquisition device could have coevolved with human (proto)language(s) given plausible assumptions, and explore the consequences of the resulting model of both language and the language faculty for theories of language change. The paper builds on the earlier work by examining the model's ability to account for the process of creolization (Bickerton, 1981; 1984; 1988; Roberts, 1998) within a selectionist theory of language change.

In this paper, I review arguments for and against the emergence and maintenance of an innate language acquisition device (LAD) via genetic assimilation. By a LAD, I mean nothing more or less than a learning mechanism which incorporates

We use historical change to explore whether children filter their input for language learning. Although others (e.g., Rohde & Plaut, 1999) have proposed filtering based on string length, we explore two types of filters that assume richer linguistic structure. One presupposes that linguistic utterances are structurally highly ambiguous and focuses learning on unambiguous data (Dresher, 1999; Fodor, 1998b; Lightfoot, 1999). The second claims that children learn only from matrix clauses (Lightfoot, 1991), defining simplicity in a structural manner. We assume that certain language changes occur via mismatches during acquisition. This allows us to use patterns of change to demonstrate that filtering restrictions are necessary to model language learning. Viewing language change as a result of mismatches during learning thus constrains the learning algorithm itself.

Naturalistic theories of language acquisition assume learners to be endowed with some innate language knowledge. The purpose of this innate knowledge is to facilitate language acquisition by constraining a learner’s hypothesis space. This paper discusses a naturalistic learning system (a Categorial Grammar Learner (CGL)) that differs from previous learners (such as the Triggering Learning Algorithm (TLA) (Gibson and Wexler, 1994)) by employing a dynamic definition of the hypothesis-space which is driven by the Bayesian Incremental Parameter Setting algorithm (Briscoe, 1999). We compare the efficiency of the TLA with the CGL when acquiring an independently and identically distributed English-like language in noiseless conditions. We show that when convergence to the target grammar occurs (which is not guaranteed), the expected number of steps to convergence for the TLA is shorter than that for the CGL initialized with uniform priors. However, the CGL converges more reliably than the TLA. We discuss the trade-off of efficiency against more reliable convergence to the target grammar. 1

The purpose of this work is to investigate the process of grammatical acquisition from data. We are using a computational learning system that is composed of a Universal Grammar with associated parameters, and a learning algorithm, following the Principles and Parameters Theory. The Universal Grammar is implemented as a Unification-Based Generalised Categorial Grammar, embedded in a default inheritance network of lexical types. The learning algorithm receives input from a corpus annotated with logical forms and sets the parameters based on this input. This framework is used as basis to investigate several aspects of language acquisition. In this paper we are concentrating on the acquisition of word order for different learners. The results obtained show the different learners having a similar performance and converging towards the target grammar given the input data available, regardless of their starting points. It also shows how the amount of noise present in the input data affects t...

Niyogi and Berwick have developed a deterministic dynamical model of language change from which they analytically derive logistic, Sshaped spread of a linguistic variant through a speech community given certain assumptions about the language learning procedure, the linguistic environment, and so forth. I will demonstrate that the same assumptions embedded in a stochastic model of language change lead to dierent and sometimes counterintuitive predictions. I will go on to argue that stochastic models are more appropriate and can support greater demographic and (psycho)linguistic realism, leading to more insightful accounts of the (putative) growth rates of attested changes. 1 Introduction It has been observed that language changes (often?) spread through a speech community following an S-shaped pattern, beginning slowly, spreading faster, then slowing o before nally extinguishing a competing variant (e.g. Weinreich et al. , 1968; Chen, 1972; Bailey, 1973:77; Lass, 1997; She...

this paper, I argue that the decorrelation argument does not undermine the account of the evolution of the language faculty via genetic assimilation nor the extended coevolutionary account in which the evolving language faculty in turn exerts linguistic selection pressure on languages (e.g. Deacon, 1997; Kirby, 1998)