Exemplar-based models of language propose that human language production and understanding operate with a store of concrete linguistic experiences rather than with abstract linguistic rules. While exemplarbased models are well acknowledged in areas like phonology and morphology, common wisdom has it that they are intrinsically flawed for syntax where infinite generative capacity is needed. This paper shows that this common wisdom is wrong. It starts out by reviewing an exemplar-based syntactic model, known as Data-Oriented Parsing, or DOP, which operates on a corpus of phrase-structure trees. While this model is productive, it is inadequate from the point of grammatical productivity. We therefore extend it to the more sophisticated linguistic representations proposed by Lexical-Functional Grammar theory, resulting in the model known as LFG-DOP, which does allow for meta-linguistic judgments of acceptability. We show how DOP deals with first language acquisition, suggesting a unified model for language learning and language use, and go into a number of syntactic phenomena that can be explained by DOP but that challenge rulebased models. We argue that if there is anything innate in language cognition it is not Universal Grammar but “Universal Representation”. 1.

The present study addresses the gradience of the dative alternation. It is shown that central evidential paradigms that have been used to support semantic explanations for the choice of dative constructions are not well founded empirically. Some widely repeated reports of intuitive contrasts in grammaticality appear to rest instead on judgments of pragmatic probabilities. An informational theory of the dative alternation is supported by the results of a corpus study on the distribution of person across dative NP and PP recipients in spoken English, and a formal model of the theory is given within the framework of stochastic Optimality Theory.

... have a high probability of occurrence given a neighboring word (Jurafsky et al. 2001). This tendency has been cited in support of the claim that probabilities are an inherent part of grammar, and of syntax in particular. There is widespread consensus, however, that the syntax of natural languages cannot be captured in terms of item-to-item transitions (Chomsky 1957). Therefore, unless one considers probabilities of syntactic structures, rather than particular combinations of neighboring words, pronunciation variation cannot be said to reflect probabilistic effects in syntax. In this article, we report a case of pronunciation variation that reflects contextual probabilities of syntactic structures. The relevant probabilities are based on the probability of a given syntactic structure, given a particular verb. We show that these probabilities affect American English /t,d/deletion, as well as the durations of words and phrases. Our results are consistent with the notion that knowledge of grammar includes knowledge of probabilities of syntactic structures, and that this knowledge affects language production. * 1. INTRODUCTION. Frequent words tend to shorten (see e.g. Schuchardt 1885, Hooper 1976), as do words that have a high probability of occurrence given a neighboring word (Jurafsky et al. 2001). This tendency has been cited as evidence for usage-based

In this work, we develop and evaluate a wide range of feature spaces for deriving Levinstyle verb classifications (Levin, 1993). We perform the classification experiments using

In this article we refine the formulation of the problem of prepositional phrase (PP) attachment as a four-way disambiguation problem. We argue that, in interpreting PPs, both knowledge about the site of the attachment (the traditional noun–verb attachment distinction) and the nature of the attachment (the distinction of arguments from adjuncts) are needed. We introduce a method to learn arguments and adjuncts based on a definition of arguments as a vector of features. In a series of supervised classification experiments, first we explore the features that enable us to learn the distinction between arguments and adjuncts. We find that both linguistic diagnostics of argumenthood and lexical semantic classes are useful. Second, we investigate the best method to reach the four-way classification of potentially ambiguous prepositional phrases. We find that whereas it is overall better to solve the problem as a single four-way classification task, verb arguments are sometimes more precisely identified if the classification is done as a two-step process, first choosing the attachment site and then labeling it as argument or adjunct. 1.

Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online corpus creation has enabled large models to be tested, revealing probabilistic constraints in processing, undermining acquisition arguments based on a perceived poverty

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This article deals with gradience in human sentence processing. We review the experimental evidence for the role of experience in guiding the decisions of the sentence processor. Based on this evidence, we argue that the gradient behavior observed in the processing of certain syntactic constructions can be traced back to the amount of past experience that the processor has had with these constructions. In modeling terms, linguistic experience can be approximated using large, balanced corpora. We give an overview of corpus-based and probabilistic models in the literature that have exploited this fact, and hence are well placed to make gradient predictions about processing behavior. Finally, we discuss a number of questions regarding the relationship between gradience in sentence processing and gradient grammaticality, and come to the conclusion that these two phenomena should be treated separately in conceptual and modeling terms.

The whisper of “does that sound ok to you? ” is a familiar sound to most linguists: we often hear it in the audience when a presenter supports a theoretical point using a judgment of grammaticality or ungrammaticality, and someone in the audience disagrees with the judgment. In recent years a growing number of

To all writers of unfinished, unread, and unstarted dissertations everywhere. ii Acknowledgements Many thanks to my advisors and my committee: Ellen Prince, to whom I and this project owe an immeasurable intellectual debt; Aravind Joshi, whose subtle yet firm nudgings in the right direction make him an invaluable mentor; Robin Clark, for comments, skepti-cism, and high scientific standards; and Matthew Stone, for useful and prompt feedback and encouragement at all points during this project and for more than once lending an or-ganization to my thoughts which was sorely needed. Numerous others have been kind enough to discuss the many issues, problems, and ques-tions I have run into during the course of this work. Their comments have enriched the content and scope of this dissertation. In particular, thanks go to Bonnie Webber, Ivana