Drug Safety Communications are used by the Food and Drug Administration to inform health care providers, patients, caregivers, and the general public about safety issues related to FDA-approved drugs. To assess patient knowledge of the messaging contained in DSCs related to the sleep aids zolpidem and eszopiclone, we conducted a large, cross-sectional patient survey of 1,982 commercially insured patients selected by stratified random sampling from the Optum Research Database who had filled at least two prescriptions for either zolpidem or eszopiclone (...) between July 1, 2012 and June 30, 2013. Among the 594 respondents, two-thirds reported hearing generally about drug safety information prior to starting a new drug, with the remaining one-third “rarely” or “never” hearing such information. Providers and pharmacists were primary sources of drug safety information. Two-thirds of zolpidem users and half of eszopiclone users reported having heard about the related DSC messages, ability to accurately identify the major factual messages was limited. Respondents reacted to new drug safety information about their sleep aids by reporting that they would want to learn about alternative ways to help them sleep and seek out more information about the safety of their specific sleeping pill. Opportunities may exist for the FDA to work with providers and pharmacies to help ensure the DSC information is more widely received and is more fully understood by those taking the affected medications. (shrink)

We formulate a Curry-typed logic with fine-grained intensionality within Turner’s typed predicate logic. This allows for an elegant presentation of a theory that corresponds to Fox and Lappin’s property theory with curry typing, but without the need for a federation of languages. We then consider how the fine-grained intensionality of this theory can be given an operational interpretation. This interpretation suggests itself as expressions in the theory can be viewed as terms in the untyped lambda-calculus, which provides a model (...) of computation. (shrink)

On the Fregean view of NP's, quantified NP's are represented as operator-variable structures while proper names are constants appearing in argument position. The Generalized Quantifier approach characterizes quantified NP's and names as elements of a unified syntactic category and semantic type. According to the Logicality Thesis, the distinction between quantified NP's, which undergo an operation of quantifier raising to yield operator-variable structures at Logical Form and non-quantified NP's, which appear in situ at LF, corresponds to a difference in logicality status. (...) The former are logical expressions while the latter are not. Using van Benthem's [2, 3] criterion for logicality, I extend the concept of logicality to GQ's. I argue that NP's modified by exception phrases constitute a class of quantified NP's which are heterogeneous with respect to logicality. However, all exception phrase NP's exhibit the syntactic and semantic properties which motivate May to treat quantified NP's as operators at LF. I present a semantic analysis of exception phrases as modifiers of GQ's, and I indicate how this account captures the central semantic properties of exception phrase NP's. I explore the consequences of the logically heterogeneous character of exception phrase NP's for proof theoretic accounts of quantifiers in natural language. The proposed analysis of exception phrase NP's provides support for the GQ approach to the syntax and semantics of NP's. (shrink)

Raquel FERN ´ ANDEZ, Jonathan GINZBURG and Shalom LAPPIN Department of Computer Science King’s College London Strand, London WC2R 2LS, UK {raquel,ginzburg,lappin}@dcs.kcl.ac.uk..

Intensional logic (IL) and its application to natural language, which the present monograph addresses, was first developed by Richard Montague in the late 1960s (e.g., Montague 1970a, 1970b). Through the efforts of (especially) Barbara Partee (e.g., Partee 1975, 1976), and Richmond Thomason, who edited the posthumous collection of Montague’s works (Thomason 1974), this became the main framework for those who aspired to a formal semantic theory for natural language, and these included computational linguists as early as Jerry Hobbs in the (...) late 1970s (e.g., Hobbs and Rosenschein 1977). In fact, until the advent of the current interest in statistical linguistics with its own conception of what semantics is, IL, or some variant of it, was perhaps the main theory of semantics within computational linguistics generally. And within current computational semantics it still is. But over the years, philosophers, linguists, and computational linguists have noted a variety of shortcomings in Montague’s version of IL. Montague defined intensions as functions from possible worlds to extensions in that world. But this had the effect of making logically equivalent expressions have the same intension, thus leading to the problem of “logical omniscience” (believing/knowing all the logical consequences of what is believed/known). Montague had based his IL on Church’s simple theory of types (Church 1940), supplemented with intensions of each type. But this implies that each natural language item accepts only arguments of some one fixed type. However, this is not true for natural language, where conjunctions, verbs, and pretty much any functional term that accepts arguments at all can accept arguments of different types. (For example, and can accept arguments that are of the sentence type, of the verb phrase type, of the adjective type, etc.; and indeed, it can accept arguments of differing types in its different argument.. (shrink)

The transformers that drive chatbots and other AI systems constitute large language models (LLMs). These are currently the focus of a lively discussion in both the scientific literature and the popular media. This discussion ranges from hyperbolic claims that attribute general intelligence and sentience to LLMs, to the skeptical view that these devices are no more than “stochastic parrots”. I present an overview of some of the weak arguments that have been presented against LLMs, and I consider several of the (...) more compelling criticisms of these devices. The former significantly underestimate the capacity of transformers to achieve subtle inductive inferences required for high levels of performance on complex, cognitively significant tasks. In some instances, these arguments misconstrue the nature of deep learning. The latter criticisms identify significant limitations in the way in which transformers learn and represent patterns in data. They also point out important differences between the procedures through which deep neural networks and humans acquire knowledge of natural language. It is necessary to look carefully at both sets of arguments in order to achieve a balanced assessment of the potential and the limitations of LLMs. (shrink)

1. Formal semantics in linguistics -- 2. Generalized quantifier theory -- 3. The interface between syntax and semantics -- 4. Anaphora, discourse, and modality -- 5. Focus, presupposition, and negation -- 6. Tense -- 7. Questions -- 8. Plurals -- 9. Computational semantics -- 10. Lexical semantics -- 11. Semantics and related domains.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of (...) a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks. (shrink)

In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within linguistic theory (...) as well. (shrink)

Sortally incorrect sentences have traditionally been referred to as "category mistakes" (Ryle ()) or "type crossings" (Drange ()). Sortal incorrectness is a ...

Learning theory has frequently been applied to language acquisition, but discussion has largely focused on information theoretic problems—in particular on the absence of direct negative evidence. Such arguments typically neglect the probabilistic nature of cognition and learning in general. We argue first that these arguments, and analyses based on them, suffer from a major flaw: they systematically conflate the hypothesis class and the learnable concept class. As a result, they do not allow one to draw significant conclusions about the learner. (...) Second, we claim that the real problem for language learning is the computational complexity of constructing a hypothesis from input data. Studying this problem allows for a more direct approach to the object of study—the language acquisition device—rather than the learnable class of languages, which is epiphenomenal and possibly hard to characterize. The learnability results informed by complexity studies are much more insightful. They strongly suggest that target grammars need to be objective, in the sense that the primitive elements of these grammars are based on objectively definable properties of the language itself. These considerations support the view that language acquisition proceeds primarily through data-driven learning of some form. (shrink)

In this paper we investigate the use of machine learning techniques to classify a wide range of non-sentential utterance types in dialogue, a necessary first step in the interpretation of such fragments. We train different learners on a set of contextual features that can be extracted from PoS information. Our results achieve an 87% weighted f-score—a 25% improvement over a simple rule-based algorithm baseline.

The question of whether it is possible to characterise grammatical knowledge in probabilistic terms is central to determining the relationship of linguistic representation to other cognitive domains. We present a statistical model of grammaticality which maps the probabilities of a statistical model for sentences in parts of the British National Corpus (BNC) into grammaticality scores, using various functions of the parameters of the model. We test this approach with a classifier on test sets containing different levels of syntactic infelicity. With (...) appropriate tuning, the classifiers achieve encouraging levels of accuracy. These experiments suggest that it may be possible to characterise grammaticality judgements in probabilistic terms using an enriched language model. (shrink)

Indirect negative evidence is clearly an important way for learners to constrain overgeneralisation, and yet a good learning theoretic analysis has yet to be provided for this, whether in a PAC or a probabilistic identification in the limit framework. In this paper we suggest a theoretical analysis of indirect negative evidence that allows the presence of ungrammatical strings in the input and also accounts for the relationship between grammaticality/acceptability and probability. Given independently justified assumptions about lower bounds on the probabilities (...) of grammatical strings, we establish that a limited number of membership queries of some strings can be probabilistically simulated. (shrink)

In this chapter we consider unsupervised learning from two perspectives. First, we briefly look at its advantages and disadvantages as an engineering technique applied to large corpora in natural language processing. While supervised learning generally achieves greater accuracy with less data, unsupervised learning offers significant savings in the intensive labour required for annotating text. Second, we discuss the possible relevance of unsupervised learning to debates on the cognitive basis of human language acquisition. In this context we explore the implications of (...) recent work on grammar induction for poverty of stimulus arguments that purport to motivate a strong bias model of language learning, commonly formulated as a theory of Universal Grammar (UG). We examine the second issue both as a problem in computational learning theory, and with reference to empirical work on unsupervised Machine Learning (ML) of syntactic structure. We compare two models of learning theory and the place of unsupervised learning within each of them. Looking at recent work on part of speech tagging and the recognition of syntactic structure, we see how far unsupervised ML methods have come in acquiring different kinds of grammatical knowledge from raw text. (shrink)