Online research in philosophy

Some expressions, such as “all” and “might”, must be interpreted differently, relative to a single context, when embedded under “says that” than when unembedded. Egan, Hawthorne and Weatherson have appealed to that fact to argue that utterance-truth is relative to point of evaluation. This paper shows that the phenomena do not warrant this relativistic response. Instead, contexts may be defined as entities that assign other contexts to contextually relevant people, and context-relative truth conditions for indirect discourse sentences can be satisfactorily formulated in terms of such contexts.

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.

Philosophers and logicians use the term “indexical” for words such as “I”, “you” and “tomorrow”. Demonstratives such as “this” and “that” and demonstratives phrases such as “this man” and “that computer” are usually reckoned as a subcategory of indexicals. (Following [Kaplan, 1989a].) The “context-dependence” of indexicals is often taken as a defining feature: what an indexical designates shifts from context to context. But there are many kinds of shiftiness, with corresponding conceptions of context. Until we clarify what we mean by “context”, this defining feature remains unclear. In sections 1–3, which are largely drawn from [Perry, forthcoming(a)], I try to clarify the sense in which indexicals are context-dependent and make some distinctions among the ways indexicals depend on context. In sections 3–6, I contrast indexicality with another phenomenon that I call “unarticulated constituents.”.

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.

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.

We argue that mathematical knowledge is context dependent. Our main argument is that on pain of distorting mathematical practice, one must analyse the notion of having available a proof, which supplies justification in mathematics, in a context dependent way.

This paper develops a general approach to contextual reasoning in natural language processing. Drawing on the view of natural language interpretation as abduction (Hobbs et al., 1993), we propose that interpretation provides an explanation of how an utterance creates a new discourse context in which its interpreted content is both true and promi- nent. Our framework uses dynamic theories of semantics and pragmatics, formal theories of context, and models of attentional state. We describe and illustrate a Prolog implementation.

Abduction was first introduced in the epistemological context of scientific discovery. It was more recently analyzed in artificial intelligence, especially with respect to diagnosis analysis or ordinary reasoning. These two fields share a common view of abduction as a general process of hypotheses formation. More precisely, abduction is conceived as a kind of reverse explanation where a hypothesis H can be abduced from events E if H is a good explanation of E. The paper surveys four known schemes for abduction that can be used in both fields. Its first contribution is a taxonomy of these schemes according to a common semantic framework based on belief revision. Its second contribution is to produce, for each non-trivial scheme, a representation theorem linking its semantic framework to a set of postulates. Its third contribution is to present semantic and axiomatic arguments in favor of one of these schemes, ordered abduction, which has never been vindicated in the literature.

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.

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?