Online research in philosophy

Kripke (1982, Wittgenstein on rules and private language. Cambridge, MA: MIT Press) presents a rule-following paradox in terms of what we meant by our past use of “plus”, but the same paradox can be applied to any other term in natural language. Many responses to the paradox concentrate on fixing determinate meaning for “plus”, or for a small class of other natural language terms. This raises a problem: how can these particular responses be generalised to the whole of natural language? In this paper, I propose a solution. I argue that if natural language is computable in a sense defined below, and the Church–Turing thesis is accepted, then this auxiliary problem can be solved.

It is a near truism of philosophy of language that sentences are prior to words--that they are the only things that fundamentally have meaning. Robert's Stainton's study interrogates this idea, drawing on a wide body of evidence to argue that speakers can and do use mere words, not sentences, to communicate complex thoughts.

Natural languages are vehicles of information, arguably the most important, certainly the most ubiquitous that humans possess. Our everyday interactions with the world, with each other and with ourselves depend on them. And even where in the specialised contexts of science we use dedicated formalisms to convey information, their use is embedded in natural language. This omnipresence of natural language is due in large part to its flexibility, which is almost always a virtue, sometimes a vice. Natural languages are able to carry information in a wide variety of ways, about a seemingly unlimited range of topics, which makes them both efficient and versatile, and hence useful in almost every circumstance. But sometimes, when pinpoint precision is what counts, this versatility can get in the..

William of Ockham's semantic theory was founded on the idea that thought takes place in a language not unlike the languages in which spoken and written communication occur. This mental language was held to have a number of features in common with everyday languages. For example, mental language has simple terms, not unlike words, out of which complex expressions can be constructed. As with words, each of these terms has some meaning, or signification; in fact Ockham held that the signification of everyday words derives precisely from the signification of mental terms. Furthermore, the meaning of a mental expression depends directly on the meaning of its constituent terms, as is the case with expressions in more familiar languages.

This essay considers what it means to understand natural language and whether a computer running an artificial-intelligence program designed to understand natural language does in fact do so. It is argued that a certain kind of semantics is needed to understand natural language, that this kind of semantics is mere symbol manipulation (i.e., syntax), and that, hence, it is available to AI systems. Recent arguments by Searle and Dretske to the effect that computers cannot understand natural language are discussed, and a prototype natural-language-understanding system is presented as an illustration.

I will argue (contra Carruthers) that accepting natural language as the format of many of our thoughts should entail accepting a version of Whorfian relativism and that, rather than something to be avoided, evidence from bilingual cognition suggests that incorporating this idea into future research would yield further insights into the cognitive functions of natural language.

Grice (1957) drew a distinction between natural(N) and non–natural(NN) meaning, and showed how the latter might be characterised in terms of intentions and the recognition of intentions. Focussing on the role of natural signs and natural behaviours in communication, this paper makes two main points. First, verbal communication often involves a mixture of natural and non–natural meaning and there is a continuum of cases between showing and meaningNN. This suggests that pragmatics is best seen as a theory of intentional verbal communication rather than a theory of meaningNN. Second, some natural behaviours have a signalling function: they are, in effect, natural codes. Such behaviours do not fit easily into Grice's distinction between natural and non–natural meaning, which suggests it is not exhaustive.

One way of describing the enterprise of natural language semantics is by analogy with interpreted formal languages, e.g. the language of arithmetic interpreted on the natural numbers. English is then the formal language consisting of the well-formed sentences of English interpreted on the structure we happen to nd around us. The business of the natural language semanticist is to describe the correlation of the formal expressions with that structure. This paper is directed against this particular interpretation 1 of the enterprise of logical natural language semantics.

Evidence is presented to show that the role of a generative grammar of a natural language is not merely to generate the grammatical sentences of that language, but also to relate them to their logical forms. The notion of logical form is to be made sense of in terms a natural logic, a logical for natural language, whose goals are to express all concepts capable of being expressed in natural language, to characterize all the valid inferences that can be made in natural language, and to mesh with adequate linguistic descriptions of all natural languages. The latter requirement imposes empirical linguistic constraints on natural logic. A number of examples are discussed.

This second edition of the book contains a new chapter on the notions of natural-kind words and natural kinds.