This article describes three statistical models for natural language parsing. The models extend methods from probabilistic context-free grammars to lexicalized grammars, leading to approaches in which a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree. Independence assumptions then lead to parameters that encode the X-bar schema, subcategorization, ordering of complements, placement of adjuncts, bigram lexical dependencies, wh-movement, and preferences for close attachment. All of these preferences are expressed by probabilities conditioned on lexical heads. The models are evaluated on the Penn Wall Street Journal Treebank, showing that their accuracy is competitive with other models in the literature. To gain a better understanding of the models, we also give results on different constituent types, as well as a breakdown of precision/recall results in recovering various types of dependencies. We analyze various characteristics of the models through experiments on parsing accuracy, by collecting frequencies of various structures in the treebank, and through linguistically motivated examples. Finally, we compare the models to others that have been applied to parsing the treebank, aiming to give some explanation of the difference in performance of the various models

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This thesis addresses situated, embodied agents interacting in complex domains. It focuses on two problems: 1) synthesis and analysis of intelligent group behavior, and 2) learning in complex group environments. Basic behaviors, control laws that cluster constraints to achieve particular goals and have the appropriate compositional properties, are proposed as effective primitives for control and learning. The thesis describes the process of selecting such basic behaviors, formally specifying them, algorithmically implementing them, and empirically evaluating them. All of the proposed ideas are validated with a group of up to 20 mobile robots using a basic behavior set consisting of: safe--wandering, following, aggregation, dispersion, and homing. The set of basic behaviors acts as a substrate for achieving more complex high--level goals and tasks. Two behavior combination operators are introduced, and verified by combining subsets of the above basic behavior set to implement collective flocking, foraging, and docking. A methodology is introduced for automatically constructing higher--level behaviors

The paper surveys work on the computational modeling of the origins and evolution of language. The main approaches are clarified and some example experiments from the domains of the evolution of communication, phonetics, lexicon formation, and syntax are discussed. 1 Introduction The paper surveys research in which software simulations and experiments with robotic agents are used to explore the viewpoint that language is a complex dynamical system. The main goal of the paper is to outline the approaches and show example experiments. Much more work needs to be done to arrive at a full-fledged theory of the origins of language and even about the work already done much more can be said than is possible in a single paper. Nevertheless, I hope to show that a new exciting approach to the study of the origins and evolution of language is taking shape. The rest of the paper is in four parts. The next section clarifies the notion of a complex system and the multi-agent perspective. Section 3...

We report a new phenomenon associated with language comprehension: the action–sentence compatibility effect (ACE). Participants judged whether sentences were sensible by making a response that required moving toward or away from their bodies. When a sentence implied action in one direction (e.g., “Close the drawer ” implies action away from the body), the participants had difficulty making a sensibility judgment requiring a response in the opposite direction. The ACE was demonstrated for three sentences types: imperative sentences, sentences describing the transfer of concrete objects, and sentences describing the transfer of abstract entities, such as “Liz told you the story. ” These data are inconsistent with theories of language comprehension in which meaning is represented as a set of relations among nodes. Instead, the data support an embodied theory of meaning that relates the meaning of sentences to human action. How language conveys meaning remains an open question. The dominant approach is to treat language as a symbol manipulation system: Language conveys meaning by using abstract, amodal, and arbitrary symbols (i.e., words) combined by syntactic rules (e.g., Burgess & Lund, 1997; Chomsky, 1980; Fodor, 2000; Kintsch, 1988; Pinker, 1994). Words are abstract in that the same word, such as “chair, ” is used for big chairs and little chairs, words are amodal in that the same word is used when chairs are spoken about or written about, and words are arbitrarily related to their referents in that the phonemic and orthographic characteristics of a word bear no relationship to the physical or functional characteristics of the word’s referent. An alternative view is that linguistic meaning is

Quartz, S. & Sejnowski, T.J. (1997). The neural basis of cognitive development: A constructivist manifesto.

The paper proposes a set of principles and a general architecture that may explain how language and meaning may originate and complexify in a group of physically grounded distributed agents. An experimental setup is introduced for concretising and validating specific mechanisms based on these principles. The setup consists of two robotic heads that watch a scene in which a robot moves around in its ecosystem. The first results from experiments showing the emergence of distinctions, of a lexicon, and of primitive syntactic structures are reported. 1 Introduction Artificial Intelligence research has made remarkable progress the last decades by showing how operations over symbolic models may explain various aspects of intelligent behavior, such as planning, problem solving, natural language processing, etc. However, the problem of the origin of these symbolic models has so far not been adequately addressed. Most of the time it is the programmer who designs formalisms and datastructures, ...

I survey some recent applications-oriented NL generation systems, and claim that despite very different theoretical backgrounds, these systems have a remarkably similar architecture in terms of the modules they divide the generation process into, the computations these modules perform, and the way the modules interact with each other. I also compare this 'consensus architecture' among applied NLG systems with psycholinguistic knowledge about how humans speak, and argue that at least some aspects of the consensns architecture seem to be in agreement with what is known about human language production, despite the fact that psycholinguistic plausibility was not in general a goal of the developers of the surveyed systems.

We present statistical analyses of the large-scale structure of three types of semantic networks: word associations, WordNet, and Roget's thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path-lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the world wide web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and also suggests one possible mechanistic basis for the effects of learning history variables (age-ofacquisition, usage frequency) on behavioral performance in semantic processing tasks.

The paper reports experiments to test the hypothesis that language is an autonomous evolving adaptive system maintained by a group of distributed agents without central control. The experiments show how a coherent lexicon may spontaneously emerge in a group of agents engaged in language games and how a lexicon may adapt to cope with new meanings that arise or new agents that enter the group. The lexicon has several characteristics of natural language lexicons, such as polysemy, synonymy and ambiguity. Keywords: origins of language, lexicon acquisition, self-organization. 1 Introduction The origins and evolution of language is still clouded in mystery, despite an extensive literature within linguistics, psychology, anthropology and neurobiology (see a recent overview in [15]). The most common hypothesis being explored in American linguistics is that language is based on a species-specific innate ability (a kind of language organ) and on the refinement of innate knowledge (universal gr...