Shows how to characterize language learning in a finite parameter space, such as in the "principles-and-parameters" approach, as a Markov structure. Explains how sample complexity varies with input distributions and learning regimes. Finds that a simple random-step algorithm always converges to the right target language and works faster than a…

Presents a model of the structure of logic considered necessary for computer processing of metaphorical language. Formally states and diagrams the algorithm for metaphors, isolating domain distance, predicate inequality, and hyperbole as particularly important factors. Distinguishes explanatory literal analogies from expressive metaphors,…

Discusses the development and progress of an international corpus of English (ICE) containing a regional corpora sampling of the standard varieties of English in countries where it is a first language, and the national varieties in countries where it is an official additional language. (GLR)

Describes the highlights of the activities and papers presented at the conference including the following: an analysis of Chinese data processing systems and Chinese sentences, natural language processing, language lesson authoring systems, character recognition, and character input. Future work in the field may include studies on Chinese…