How to Do Things With Words

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

Language, Thought, and Other Biological Categories

Rules and Representations

This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al. (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.’s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.

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Neural Network Acceptability Judgments

http://www.terpconnect.umd.edu/%7Epietro/research/papers/POS.pdf

Poverty of the stimulus revisited.

L'A. souleve le probleme de la vacuite des lois ceteris paribus qui, affirmant l'egalite des autres choses, peuvent etre reformulees dans le sens des presupposes des lois reelles qui n'admettent aucun contre-exemple. A ces rares reformulations, l'A. oppose une condition suffisante pour la non-vacuite des lois de la parite, dont les affirmations sont aussi verifiables que les autres affirmations empiriques

When Other Things Aren't Equal: Saving Ceteris Paribus Laws from Vacuity

Introduction 1 Elementary Cases 2 Quantification and Plurality 3 Causal Verbs and Sentential Complements Tentative Conclusions and Promissory Notes References Index

Events and Semantic Architecture

In just a few years, children achieve a stable state of linguistic competence, making them effectively adults with respect to: understanding novel sentences, discerning relations of paraphrase and entailment, acceptability judgments, etc. One familiar account of the language acquisition process treats it as an induction problem of the sort that arises in any domain where the knowledge achieved is logically underdetermined by experience. This view highlights the ‘cues’ that are available in the input to children, as well as childrens skills in extracting relevant information and forming generalizations on the basis of the data they receive. Nativists, on the other hand, contend that language-learners project beyond their experience in ways that the input does not even suggest. Instead of viewing language acqusition as a special case of theory induction, nativists posit a Universal Grammar, with innately specified linguistic principles of grammar formation. The ‘nature versus nurture’ debate continues, as various “poverty of stimulus” arguments are challenged or supported by developments in linguistic theory and by findings from psycholinguistic investigations of child language. In light of some recent challenges to nativism, we rehearse old poverty-of stimulus arguments, and supplement them by drawing on more recent work in linguistic theory and studies of child language.

https://terpconnect.umd.edu/%7Epietro/research/papers/NNUG.pdf

Nature, Nurture and Universal Grammar

The meaning of 'most' can be described in many ways. We offer a framework for distinguishing semantic descriptions, interpreted as psychological hypotheses that go beyond claims about sentential truth conditions, and an experiment that tells against an attractive idea: 'most' is understood in terms of one-to-one correspondence. Adults evaluated 'Most of the dots are yellow', as true or false, on many trials in which yellow dots and blue dots were displayed for 200 ms. Displays manipulated the ease of using a 'one-to-one with remainder' strategy, and a strategy of using the Approximate Number System to compare of (approximations of) cardinalities. Interpreting such data requires care in thinking about how meaning is related to verification. But the results suggest that 'most' is understood in terms of cardinality comparison, even when counting is impossible. How is the word 'most' related to human capacities for detecting and comparing numerosities? One might think the answer is obvious, and explicit in standard semantic theories: 'most' is understood in terms of a capacity to compare cardinal numbers; e.g. 'Most of the dots are yellow' means that the number of yellow dots is greater than the number of nonyellow dots. But there are other possibilities for how competent speakers understand 'most', and we offer experimental evidence that tells against some initially attractive hypotheses. By discussing one lexical item in this way, we hope to illustrate how semantics and psychology can and should be pursued in tandem, especially with regard to the capacities that let humans become numerate. Following common practice in semantics, we begin by characterizing the contribution of 'most' to the truth conditions of sentences that have the following form: Most (of the)� sa re� . At this level of analysis, there are many equivalent characterizations, as discussed below. Our aim is to give some of these formal distinctions empirical bite, in a way that permits adjudication of distinct hypotheses about how speakers understand 'most' (cf. Hackl, 2009). In short, we want to know how speakers represent the truth conditions in question.

/pdf/the-meaning-of-most-semantics-numerosity-and-psychology-592aoztg2a.pdf

Paul M. Pietroski

Papers

Poverty of the stimulus revisited.

When Other Things Aren't Equal: Saving Ceteris Paribus Laws from Vacuity

Events and Semantic Architecture

Nature, Nurture and Universal Grammar

The Meaning of ‘Most’: Semantics, Numerosity and Psychology