In this thesis I present various algorithms for the unsupervised machine learning of aspects of natural languages using a variety of statistical models. The scientific object of the work is to examine the validity of the so-called Argument from the Poverty of the Stimulus advanced in favour of the proposition that humans have language-specific innate knowledge. I start by examining an a priori argument based on Gold's theorem, that purports to prove that natural languages cannot be learned, and some formal issues related to the choice of statistical grammars rather than symbolic grammars. I present three novel algorithms for learning various parts of natural languages: first, an algorithm for the induction of syntactic categories from unlabelled text using distributional information, that can deal with ambiguous and rare words; secondly, a set of algorithms for learning morphological processes in a variety of languages, including languages such as Arabic with nonconcatenative morphology; thirdly an algorithm for the unsupervised induction of a context-free grammar from tagged text. I carefully examine the interaction between the various components, and show how these algorithms can form the basis for a empiricist model of language acquisition. I therefore conclude that the Argument from the Poverty of the Stimulus is unsupported by the evidence.

Abstract not found

John Maynard Smith and Eörs Szathmáry argued that human language signified the eighth major transition in evolution: human language marked a new form of information transmission from one generation to another [Maynard Smith J, Szathmáry E. The major transitions in evolution. Oxford: Oxford Univ. Press; 1995]. According to this view language codes cultural information and as such forms the basis for the evolution of complexity in human culture. In this article we develop the theory that language also codes information in another sense: languages code information on their own structure. As a result, languages themselves provide information that influences their own survival. To understand the consequences of this theory we discuss recent computational models of linguistic evolution. Linguistic evolution is the process by which languages themselves evolve. This article draws together this recent work on linguistic evolution and highlights the significance of this process in understanding the evolution of linguistic complexity. Our conclusions are that: (1) the process of linguistic transmission constitutes the basis for an evolutionary system, and (2), that this evolutionary system is only superficially comparable to the process of

Based on the apparent paucity of input, and the nonobvious nature of linguistic generalizations, Chomskyan linguists assume an innate body of linguistically detailed knowledge, known as Universal Grammar (UG), and attribute to it principles required to account for those “properties of language that can reasonably be supposed not to have been learned ” (Chomsky, 1975). A definitive account of learnability is lacking, but is implicit in examples of the application of the logic. Our research demonstrates, however, that important statistical properties of the input have been overlooked, resulting in UG being credited for properties which are demonstrably learnable; in contradiction to Chomsky’s celebrated argument for the innateness of structure-dependence (e.g. Chomsky, 1975), a simple recurrent network (Elman, 1990), given input modelled on child-directed speech, is shown to learn the structure of relative clauses, and to generalize that structure to subject position in aux-questions. The result demonstrates that before a property of language can reasonably be supposed not to have been learned, it is necessary to give greater consideration to the indirect positive evidence in the data and that connectionism can be invaluable to linguists in that respect.

This thesis aims to develop a psychologically plausible account of concepts by integrating key insights from philosophy (on the metaphysical basis for concept possession) and psychology (on the mechanisms underlying concept acquisition). I adopt an approach known as informational atomism, developed by Jerry Fodor. Informational atomism is the conjunction of two theses: (i) informational semantics, according to which conceptual content is constituted exhaustively by nomological mind–world relations; and (ii) conceptual atomism, according to which (lexical) concepts have no internal structure. I argue that informational semantics needs to be supplemented by allowing content-constitutive rules of inference (“meaning postulates”). This is because the content of one important class of concepts, the logical terms, is not plausibly informational. And since, it is argued, no principled distinction can be drawn between logical concepts and the rest, the problem that this raises is a general one.

Abstract not found

Abstract We show how cultural selection for learnability during the process of linguistic evolution can be visualized using a simple iterated learning model. Computational models of linguistic evolution typically focus on the nature of, and conditions for, stable states. We take a novel approach and focus on understanding the process of linguistic evolution itself. What kind of evolutionary system is this process? Using visualization techniques, we explore the nature of replicators in linguistic evolution, and argue that replicators correspond to local regions of regularity in the mapping between meaning and signals. Based on this argument, we draw parallels between phenomena observed in the model and linguistic phenomena observed across languages. We then go on to identify issues of replication and selection as key points of divergence in the parallels between the processes of linguistic evolution and biological evolution.

The Baldwin effect occurs, if it ever does, when a biological trait becomes innate as a result of first being learned. Suppose that some trait is initially absent from a population of organisms. Then a number of organisms succeed in learning the trait. There will be a Baldwin effect if this period of learning leads to the trait becoming

The ‘Generative ’ program for linguistic theory is now about 50 years old. During its short history, the aims and methods of the program have changed, as is to be expected of any scientific approach to natural phenomena. This essay outlines three periods within the Generative enterprise. The phases can be (roughly) identified in terms of the different goals that generativists set for themselves; each bringing with it different standards of success and suggesting (somewhat) different research agendas. All three goals are still with us and animate related yet different kinds of linguistic investigation. Thus, getting some clarity on these historical periods might also serve to clarify current practice. The three phases, or periods that we wish to consider can be called (i) the Combinatoric, (ii) the Cognitive, and (iii) the Minimalist. Each offers conceptual parallelisms with (and looks to inspiration from) better-developed sciences. Thus, the Combinatoric phase ‘connects ’ at some level with engineering, the Cognitive phase with biology, and the Minimalist with physics. Each period is also associated with a central text (or two) by Noam

The proposal that the concept of innateness expresses a ‘folk biological ’ theory of the ‘inner natures ’ of organisms was tested by examining the response of biologically naive participants to a series of realistic scenarios concerning the development of birdsong. Our results explain the intuitive appeal of many of the existing philosophical analyses of the innateness concept. They simultaneously explain why all such analyses are subject to compelling counterexamples. We argue that this explanation undermines the appeal of these analyses, whether understood as analyses of the vernacular concept or as explications of that concept for the purposes of science. 1.