"Normality" in Update Semantics

No abstract

Word meaning and lexical pragmatics

In spite of their differences, Two-level Conceptual Semantics, Generative Lexicon Theory and Relevance Theory also have similarities with respect to treatment of the relation of word meanings and contexts. Therefore, the three theories can be considered as complementing each other in analysing word meanings in utterances. In the present paper I will outline a conception of lexical pragmatics which critically amalgamates the views of these theories and has more explanatory power than each theory does separately. Such a lexical pragmatic conception accepts lexical-semantic representations which can be radically underspecified and allow for other methods of meaning description than componential analysis. As words have underspecified meaning representations, they reach their full meanings in corresponding contexts (immediate or extended) through considerable pragmatic inference. The Cognitive Principle of Relevance regulates the way in which the utterance meaning is construed

Some aspects of optimality in natural language interpretation

In a series of papers, Petra Hendriks, Helen de Hoop, and Henriette de Swart Lave applied optimality theory (OT) to semantics. These authors argue that there is a fundamental difference between the form of OT as used in syntax on the one hand and its form as used in semantics on the other hand. Whereas in the first case OT takes the point of view of the speaker, in the second case the point of view of the hearer is taken. The aim of this paper is to argue that the proper treatment of OT in natural language interpretation has to take both perspectives at the same time. A conceptual framework is established that realizes the integration of both perspectives. It will be argued that this framework captures the essence of the Gricean maxims and gives a precise explication of Atlas & Levinson's (1981) idea of balancing between informativeness and efficiency in natural language processing. The ideas are then applied to resolve some puzzles in natural language interpretation.

Neural networks, penalty logic and optimality theory

Ever since the discovery of neural networks, there has been a controversy between two modes of information processing. On the one hand, symbolic systems have proven indispensable for our understanding of higher intelligence, especially when cognitive domains like language and reasoning are examined. On the other hand, it is a matter of fact that intelligence resides in the brain, where computation appears to be organized by numerical and statistical principles and where a parallel distributed architecture is appropriate. The present claim is in line with researchers like Paul Smolensky and Peter Gärdenfors and suggests that this controversy can be resolved by a unified theory of cognition – one that integrates both aspects of cognition and assigns the proper roles to symbolic computation and numerical neural computation. The overall goal in this contribution is to discuss formal systems that are suitable for grounding the formal basis for such a unified theory. It is suggested that the instruments of modern logic and model theoretic semantics are appropriate for analyzing certain aspects of dynamical systems like inferring and learning in neural networks. Hence, I suggest that an active dialogue between the traditional symbolic approaches to logic, information and language and the connectionist paradigm is possible and fruitful. An essential component of this dialogue refers to Optimality Theory (OT) – taken as a theory that likewise aims to overcome the gap between symbolic and neuronal systems. In the light of the proposed logical analysis notions like recoverability and bidirection are explained, and likewise the problem of founding a strict constraint hierarchy is discussed. Moreover, a claim is made for developing an "embodied" OT closing the gap between symbolic representation and embodied cognition