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.

It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to be rooted in a language-specific biological endowment, which encodes universal, but arbitrary, principles of language structure (a universal grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes. The resulting puzzle concerning the origin of UG we call the logical problem of language evolution. Because the processes of language change are much more rapid than processes of genetic change, language constitutes a “moving target ” both over time and across different human populations, and hence cannot provide a stable environment to which UG genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG—the mesh between learners and languages—arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent “organism, ” which evolves under selectional pressures from human learning and processing mechanisms. That is, languages are themselves undergoing severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases, independent of language. We illustrate how this framework can integrate evidence from different literatures and methodologies to explain core linguistic phenomena, including binding constraints, word order universals, and diachronic language change. 1.

www.lti.cs.cmu.edu

How children go about learning the general regularities that govern language, as well as keeping track of the exceptions to them, remains one of the challenging open questions in the cognitive science of language. Computational modeling is an important methodology in research aimed at addressing this issue. We must determine appropriate learning mechanisms that can grasp generalizations from examples of specific usages, and that exhibit patterns of behaviour over the course of learning similar to those in children. Early learning of verb argument structure is an area of language acquisition that provides an interesting testbed for such approaches due to the complexity of verb usages. A range of linguistic factors interact in determining the felicitous use of a verb in various constructions—associations between syntactic forms and properties of meaning, that form the basis for a number of linguistic and psycholinguistic theories of language. We present a computational model for the representation, acquisition, and use of verbs and constructions. Our Bayesian framework is founded on a novel view of constructions as a probabilistic association between syntactic and semantic features. The computational experiments reported here demonstrate the feasibility of learning general constructions, and their exceptions, from individual usages of verbs. The behaviour of the model over the timecourse of acquisition mimics in relevant aspects the stages of learning exhibited by children. Our proposal thus sheds light on the possible mechanisms at work in forming linguistic generalizations and maintaining knowledge of exceptions. 1

We address the challenge of automatically generating questions from reading materials for educational practice and assessment. Our approach is to overgenerate questions, then rank them. We use manually written rules to perform a sequence of general purpose syntactic transformations (e.g., subject-auxiliary inversion) to turn declarative sentences into questions. These questions are then ranked by a logistic regression model trained on a small, tailored dataset consisting of labeled output from our system. Experimental results show that ranking nearly doubles the percentage of questions rated as acceptable by annotators, from 27 % of all questions to 52 % of the top ranked 20 % of questions. 1

We develop an approach to automatically identify the most probable multi-word constructions used in children’s utterances, given syntactically annotated utterances from the Brown corpus of CHILDES. The found constructions cover many interesting linguistic phenomena from the language acquisition literature, and show a progression from very concrete towards abstract constructions. We show quantitatively that for all children of the Brown corpus grammatical abstraction, defined as the relative number of variable slots in the productive units of their grammar, increases globally with age.

This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: (i) how agents learn and represent compositional actions; (ii) how agents learn and represent compositional lexicons; (iii) the dynamics of social interaction and learning; and (iv) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test-scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.

can be used to describe events of transfer, show two options for expressing their arguments, jointly referred to as the dative alternation, illustrated in (1)-(3) with English data. (1) a. Terry gave Sam an apple b. Terry gave an apple to Sam (2) a. Martha sent Myrna a package b. Martha sent a package to Myrna (3) a. Leigh threw Lane the ball b. Leigh threw the ball to Lane In a recent paper, M. Rappaport Hovav and B. Levin (2008) challenge the predominant view of the English dative alternation, which takes all alternating verbs to have two meanings and, concomitantly, associates each meaning with a particular syntactic realization (e.g. S. Beck and K. Johnson 2004, G. Green 1974,

Since the 1990s, more and more linguistic articles have been published in the framework of Construction Grammar. Although Kay and Fillmore (1999, p. 19) make it clear that Constructions are not necessarily phrasal, most of the authors suggest phrasal Constructions. This is especially apparent in Construction Grammar-inspired studies in the HPSG framework. In what follows, I show that the difference between phrasal approaches and lexical approaches is not as great as is sometimes claimed, although selecting one approach over the other may nevertheless have serious consequences. This discussion focuses on resultative constructions, a phenomenon for which both phrasal and lexical analyses have been suggested. I show that a considerable number of different Constructions must be postulated to account for all the patterns that may arise from the interaction of the resultative construction with both constituent reordering and valence changing processes. It is shown that adjuncts, predicate complexes, and derivational morphology pose considerable problems for the phrasal approach, while they are unproblematic for lexical rule-based approaches. The discussion is relevant for all frameworks that do not use transformations to map phrasal configurations to other phrasal configurations.

constructions