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.     

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.     

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.     

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.     

Providing machine tractable dictionary tools

Machine readable dictionaries (Mrds) contain knowledge about language and the world essential for tasks in natural language processing (Nlp). However, this knowledge, collected and recorded by lexicographers for human readers, is not presented in a manner for Mrds to be used directly for Nlp tasks. What is badly needed are machine tractable dictionaries (Mtds): Mrds transformed into a format usable for Nlp. This paper discusses three different but related large-scale computational methods to transform Mrds into Mtds. The Mrd used is The Longman Dictionary of Contemporary English (Ldoce). The three methods differ in the amount of knowledge they start with and the kinds of knowledge they provide. All require some handcoding of initial information but are largely automatic. Method I, a statistical approach, uses the least handcoding. It generates relatedness networks for words in Ldoce and presents a method for doing partial word sense disambiguation. Method II employs the most handcoding because it develops and builds lexical entries for a very carefully controlled defining vocabulary of 2,000 word senses (1,000 words). The payoff is that the method will provide an Mtd containing highly structured semantic information. Method III requires the handcoding of a grammar and the semantic patterns used by its parser, but not the handcoding of any lexical material. This is because the method builds up lexical material from sources wholly within Ldoce. The information extracted is a set of sources of information, individually weak, but which can be combined to give a strong and determinate linguistic data base.     

Is Word Sense Disambiguation Just One More NLP Task?

The paper examines the task of Word Sense Disambiguation (WSD) criticallyand compares it with Part of Speech (POS) tagging, arguing that the abilityof a writer to create new senses distinguishes the tasks and makes it moreproblematic to test WSD by the mark-up-and-model paradigm, because newsenses cannot be marked up against dictionaries. This serves to set WSDapart and puts limits on its effectiveness as an independent NLP task.Moreover, it is argued that current WSD methods based on very small wordsamples are also potentially misleading because they may or may not scaleup. Since all-word WSD methods are now available and are producing figurescomparable to the smaller scale tasks, it is argued that we shouldconcentrate on the former and find ways of bootstrapping test materialsfor such tests in the future.     

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.