The paper proposes a theory relating syntax, semantics, and intonational prosody, and covering a wide range of English intonational tunes and their semantic interpretation in terms of focus and information structure. The theory is based on a version of combinatory categorial grammar which directly pairs phonological and logical forms without intermediary representational levels.

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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...

We describe a bottom-up chart realization algorithm adapted for use with Combinatory Categorial Grammar (CCG), and show how it can be used to efficiently realize a wide range of coordination phenomena, including argument cluster coordination and gapping. The algorithm has been implemented as an extension to the OpenNLP open source CCG parser. As an avenue for future exploration, we also suggest how the realizer could be used to simplify the treatment of aggregation in conjunction with higher level content planning components.

We describe a novel training criterion for probabilistic grammar induction models, contrastive estimation [Smith and Eisner, 2005], which can be interpreted as exploiting implicit negative evidence and includes a wide class of likelihood-based objective functions. This criterion is a generalization of the function maximized by the Expectation-Maximization algorithm [Dempster et al., 1977]. CE is a natural fit for log-linear models, which can include arbitrary features but for which EM is computationally difficult. We show that, using the same features, log-linear dependency grammar models trained using CE can drastically outperform EMtrained generative models on the task of matching human linguistic annotations (the MATCHLIN-GUIST task). The selection of an implicit negative evidence class—a “neighborhood”—appropriate to a given task has strong implications, but a good neighborhood one can target the objective of grammar induction to a specific application. 1

This thesis is about estimating probabilistic models to uncover useful hidden structure in data; specifically, we address the problem of discovering syntactic structure in natural language text. We present three new parameter estimation techniques that generalize the standard approach, maximum likelihood estimation, in different ways. Contrastive estimation maximizes the conditional probability of the observed data given a “neighborhood” of implicit negative examples. Skewed deterministic annealing locally maximizes likelihood using a cautious parameter search strategy that starts with an easier optimization problem than likelihood, and iteratively moves to harder problems, culminating in likelihood. Structural annealing is similar, but starts with a heavy bias toward simple syntactic structures and gradually relaxes the bias. Our estimation methods do not make use of annotated examples. We consider their performance in both an unsupervised model selection setting, where models trained under different initialization and regularization settings are compared by evaluating the training objective on a small set of unseen, unannotated development data, and supervised model selection, where the most accurate model on the development set (now with annotations)

An account of parameter setting during grammatical acquisition is presented in terms of Generalized Categorial Grammar embedded in a multiple default inheritance hierarchy, providing a natural partial ordering on the setting of parameters (Briscoe, 1997a). Experiments reported show that several experimentally effective learners can be defined in this framework capable of reliably acquiring a grammar from a sequence of triggers drawn from one of 70 full languages (or the 200+ more restricted subset languages of these full languages). Evolutionary computational simulations of evolving populations of such language learners/users suggest that: 1) languages evolve towards greater learnability, interpretability and/or expressivity; 2) learning procedures evolve towards more efficient variants depending on the linguistic environment of adaptation. The reciprocal evolution of language learning procedures and of language creates a genuinely coevolutionary dynamic, despite the relative speed of ...

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.

A new account of parameter setting during grammatical acquisition is presented in terms of Generalized Categorial Grammar embedded in a default inheritance hierarchy, providing a natural partial ordering on the setting of parameters. Experiments show that several experimentally effective learners can be defined in this framework. Evolutionary simulations suggest that a learner with default initial settings for parameters will emerge, provided that learning is memory limited and the environment of linguistic adaptation contains an appropriate language.

Human language acquisition, and in particular the acquisition of grammar, is a partially-canalized, strongly-biased but robust and e cient procedure. For example, children prefer to induce lexically compositional rules (e.g. Wanner and Gleitman, 1982) despite the use, in every attested human language, of constructions, such as morphological negation or non-compositional idioms. And, most parameters of grammatical variation set during language acquisition appear to have default or so-called unmarked values retained in the absence of robust counter-evidence (e.g. Bickerton, 1984 � Hyams, 1986 � Lightfoot, 1992). A variety of explanations have been o ered for the emergence of a partially-innate language acquisition device (LAD) with such properties based on saltation (Berwick, 1998 � Bickerton, 1990, 1998) or genetic assimilation (Pinker and Bloom, 1990). But none provide a coherent detailed account of both the emergence and maintenance of a LAD in an evolving population. The account proposed here is that a minimal LAD emerged via recruitment of general-purpose (Bayesian) learning mechanisms (e.g. Staddon, 1988 � Cosmides and Tooby, 1996) to a speci cally-linguistic mental representation capable of expressing mappings from the `language of thought ' to realizable, essentially linearized, encodings of propositions of the language of thought. However, the selective pressure favouring such adevelopment, and its subsequent maintenance and re nement, is only coherent given a coevolutionary scenario in which a (proto)language supporting successful communication within a population had already itself evolved on a historical timescale (e.g. Hurford, 1987 � Kirby, 1998 � Steels, 1998) and continued to coevolve with the LAD (e.g. Briscoe, 1997, 1998a,b). The model of the LAD presented here builds on and extends previous work in the parameter setting