Online research in philosophy

Although context-processing deficits may be core features of schizophrenia, context remains a poorly defined concept. To test Phillips & Silverstein's model, we need to operationalize context more precisely. We offer several useful ways of framing context and discuss enhancing or facilitating schizophrenic patients' performance under different contextual situations. Furthermore, creativity may be a byproduct of cognitive uncoordination.

We focus on how we should define the relevance of information to a context for information processing agents, such as oracles. We build our formalization of relevance upon works in pragmatics which refer to contextual information without giving any explicit representation of context. We use a formalization of context (due to us) in Situation Theory, and demonstrate its power in this task. We also discuss some computational aspects of this formalization.

The aim of this paper is to describe a simple extension of semantic nets. In this formulation we have labelled nodes with directed arcs, but the directed arcs can lead to other arcs as well as nodes. In this model contexts are not differentiated as special objects, but rather that some nodes to a greater or lesser extent have roles as encoders of contextual information. This formulation is shown to be expressive enough to capture several aspects of context, namely: context-dependent inference, context specific learning, the selection of a relevant context and the generalisation of knowledge. Its strengths are its simplicity, the fact that it can relate and integrate several aspects of context and its connections with formal logic. It is not claimed that this is a model of any type of context found in human activity.

In our routine communicative activities, context is exploited both in production and in comprehension, and is strictly related to another problematic notion, viz. meaning. Thus Bateson (1979: 15): ‘‘Without context, words and actions have no meaning at all. This is true not only of human communication in words but also of all communication whatsoever, of all mental process, of all mind, including that which tells the sea anemone how to grow and the amoeba what he should do next.’’.

The papers in this volume represent the views of a range of experts in a variety of language-related disciplines on the role which context plays in language learning and language understanding. The authors provide various theoretical constructs which help impose order on the apparent chaos of contextual factors which may have an influence on the production and comprehension of speech events. They focus on a variety of types of context, including the context established by different speech communities, interpersonal contexts, the classroom context, and the context provided by the linguistic code itself. The papers illustrate how the treatment of context varies across the disciplines of linguistics, historical stylistics, applied linguistics, and psycholinguistics. Each paper is prefaced by an editorial introduction to help the reader trace out common themes and points of conflict.

The notion of context arises in assorted areas of artificial intelligence (AI), including knowledge representation, natural language processing, intelligent information retrieval, etc. Although the term ‘context’ is frequently employed in descriptions, explanations, and analyses of computer programs in these areas, its meaning is frequently left to the reader’s understanding. In other words, it is used in an intuitive manner. In an influential paper, Clark and Carlson (1981) state that context has become a favourite word. They then complain that the denotation of the word has become murkier as its uses have been extended in many directions, making context some sort of ‘conceptual garbage can.’.

All sorts of things are context-dependent in one way or another. What it is appropriate to wear, to give, or to reveal depends on the context. Whether or not it is all right to lie, harm, or even kill depends on the context. If you google the phrase ‘depends on the context’, you’ll get several hundred million results. This chapter aims to narrow that down. In this context the topic is context dependence in language and its use. It is commonly observed that the same sentence can be used to convey different things in different contexts. That is why people complain when something they say is ‘taken out of context’ and insist that it be ‘put into context’, because ‘context makes it clear’ what they meant. Indeed, it is practically a platitude that what a speaker means in uttering a certain sentence, as well as how her audience understands her, ‘depends on the context’. But just what does that amount to, and to what extent is it true?

The issue of context arises in assorted areas of Artificial Intelligence. Although its importance is realized by various researchers, there is not much work towards a useful formalization. In this paper, we will present a preliminary model (based on Situation Theory) and give examples to show the use of context in various fields, and the advantages gained by the acceptance of our proposal.

At the heart of natural language processing is the understanding of context dependent meanings. This paper presents a preliminary model of formal contexts based on situation theory. It also gives a worked-out example to show the use of contexts in lifting, i.e., how propositions holding in a particular context transform when they are moved to another context. This is useful in NLP applications where preserving meaning is a desideratum.

The use of context can considerably facilitate reasoning by restricting the beliefs reasoned upon to those relevant and providing extra information specific to the context. Despite the use and formalization of context being extensively studied both in AI and ML, context has not been much utilized in agents. This may be because many agents are only applied in a single context, and so these aspects are implicit in their design, or it may be that the need to explicitly encode information about various contexts is onerous. An algorithm to learn the appropriate context along with knowledge relevant to that context gets around these difficulties and opens the way for the exploitation of context in agent design. The algorithm is described and the agents compared with agents that learn and apply knowledge in a generic way within an artificial stock market. The potential for context as a principled manner of closely integrating crisp reasoning and fuzzy learning is discussed.