Do native predators feed on non-native prey? The case of round goby in a fluvial piscivorous fish assemblage

We evaluated whether or not the invasive round goby (Neogobius melanostomus) represents an important prey source for seven native fish predators in Lake St. Pierre (St. Lawrence River, Canada). The frequency of occurrence of round goby in the stomach contents of brown bullhead (Ameiurus nebulosus) and channel catfish (Ictalurus punctatus) was very low (< 5%), while for the five other predators, it varied between 22% (yellow perch; Perca flavescens) and 65% (sauger; Sander canadensis). Several competing models linking the probability of occurrence of round goby in stomach contents to variables related to space, physical habitat, biotic interactions and predator size were tested for the five species feeding on round goby. Results indicated that space variables influenced round goby occurrence in stomachs for all species. In addition, physical habitat variables had an influence for sauger and walleye (Sander vitreus); biotic variables had an influence for yellow perch, walleye and sauger; and size had an influence for northern pike (Esox lucius), smallmouth bass (Micropterus dolomieu) and walleye. These results are discussed in light of known biological features of the round goby and native predators studied here and have important implications in terms of understanding round goby invasion success in the Great Lakes–St. Lawrence system.     

Karen Sp?rck Jones (1935-2007) [In Memoriam]

In memory of Karen Sp?rck Jones, who contributed significantly to the information retrieval and natural language processing fields and, in her later years, was concerned with their relationship within general schemes of representation in AI. Also remembered is Jay Modi (1975-2007), an outstanding teacher and a rising star in the AI community.     

Making preferences more active

The paper discusses the incorporation of richer semantic structures into the Preference Semantics system: they are called pseudo-texts and capture something of the information expressed in one type of frame proposed by Minsky (q.v.). However, they are in a format, and subject to rules of inference, consistent with earlier accounts of this system of language analysis and understanding. Their use is discussed in connection with the phenomenon of extended use: sentences where the semantic preferences are broken. It is argued that such situations are the norm and not the exception in normal language use, and that a language understanding system must give some general treatment of them. A notion of sense projection is proposed, leading on to an alteration of semantic formulas (word sense representations) in the face of unexpected context by drawing information from the pseudo texts. A possible implementation is described, based on a new semantic parser for the Preference Semantics system, which would cope with extended use by the methods suggested and answer questions about the process of analysis itself. It is argued that this would be a good context in which to place a language understander (rather than that of question-answering about a limited area of the real world, as is normal) and, moreover, that the sense projection mechanisms suggested would provide a test-bed on which the usefulness of frames for language understanding could be realistically assessed.     

Approaches to natural language discourse processing

One of the most difficult problems within the field of Artificial Intelligence (AI) is that of processing language by computer, or natural-language processing. A major problem in natural-language processing is to build theories and models of how individual utterances cling together into a coherent discourse. The problem is important because, to properly understand natural language, a computer should have some sense of what it means for a discourse to be coherent and rational. Theories, models and implementations of natural-language processing argue for a measure of coherence based on three themes: meaning, structure, and intention. Most approaches stress one theme over all the others. Their future lies in the integration of components of all approaches. A theory of intention analysis solves, in part, the problem of natural-language dialogue processing. A central principle of the theory is that coherence of natural-language dialogue can be modelled by analysing sequences of intention. The theory of intention analysis has been incorporated within a computational model, called Operating System CONsultant (OSCON), implemented in Quintus Prolog, which understands, and answers in English, English questions about computer operating systems. Theories and implementations of discourse processing will not only enable people to communicate better with computers, but also enable computers to better communicate with people.     

Tailoring Automatically Generated Hypertext

This paper describes an approach for tailoring the content and structure of automatically generated hypertext. The implemented system HYLITE is based on applied Natural Language Generation (NLG) techniques, a re-usable user modelling component (VIEWGEN), and a flexible architecture with module feedback. The user modelling component is used by the language generation modules to adapt the hypertext content and links to user beliefs and preferences and to the previous interaction. Unlike previous adaptive NLG systems, which have their own, application-specific user models, HYLITE has re-used a generic agent modelling framework (VIEWGEN) instead. Apart from avoiding the development costs of a new model, this also enabled a more extendable system architecture. Another distinct feature of our approach is making NLG techniques adaptable by the user, i.e., providing users with control over the user model and the hypertext adaptivity.     

Language,vision and metaphor

The integration of language and vision capabilities in computers can be seen purely as a multi-media task without any theoretical assumptions being required. However, it is worth exploring whether the modalities have anything serious in common, in particular in the light of claim that most non-technical language use is metaphorical. What consequences would that have for the underlying relationship of language and vision: is it possible that vision is largely metaphorical? The conclusion (see also, Wilks 1978b and Wilks and Okada (in press) is that visual processing can embody structural ambiguity (whether compositional or not), but not anything analogous to metaphor. Metaphor is essentially connected with the extension of sense and only symbols can have senses. But if it makes no sense to say a figure can be metaphorical (unless it embodies symbolic elements) that must also mean, alas, that it makes no sense to say it is literally anything either. Only a symbol can be literally something. A hat is a hat is a hat, but never, ever literally so.