The article argues that cognitive linguistic theory may prove an alternative to the Montague paradigm for designing natural language understanding systems. Within this framework it describes a system which models language understanding as a dialogical process between user and computer. The system operates with natural language texts as input and represent language meaning as entity-relationship diagrams.

This article contains the results of a theoretical analysis of the phenomenon of natural language understanding (NLU), as a methodological problem. The combination of structural-ontological and informational-psychological approaches provided an opportunity to describe the subject matter field of NLU, as a composite function of the mind, which systemically combines the verbal and discursive structural layers. In particular, the idea of NLU is presented, on the one hand, as the relation between the discourse of a specific speech message (...) and the meta-discourse of a language, in turn, activated by the need-motivational factors. On the other hand, it is conceptualized as a process with a specific structure of information metabolism, the study of which implies the necessity to differentiate the affective (emotional) and need-motivational influences on the NLU, as well as to take into account their interaction. At the same time, the hypothesis about the influence of needs on NLU under the scenario similar to the pattern of Yerkes-Dodson is argued. And the theoretical conclusion that emotions fulfill the function of the operator of the structural features of the information metabolism of NLU is substantiated. Thus, depending on the modality of emotions in the process of NLU, it was proposed to distinguish two scenarios for the implementation of information metabolism - reduction and synthetic. The argument in favor of the conclusion about the productive and constitutive role of emotions in the process of NLU is also given. (shrink)

From a leading authority in artificial intelligence, this book delivers a synthesis of the major modern techniques and the most current research in natural language processing. The approach is unique in its coverage of semantic interpretation and discourse alongside the foundational material in syntactic processing.

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. (shrink)

The notion of modularity, introduced by Noam Chomsky and developed with special emphasis on perceptual and linguistic processes by Jerry Fodor in his important book The Modularity of Mind, has provided a significant stimulus to research in cognitive science. This book presents essays in which a diverse group of philosophers, linguists, psycholinguists, and neuroscientists - including both proponents and critics of the modularity hypothesis - address general questions and specific problems related to modularity. Jay L. Garfield is Associate Professor of (...) Philosophy in the School of Communications and Cognitive Science at Hampshire College. (shrink)

An Example for Natural Language Understanding and the AI Problems it Raises I think this 1976 memorandum is of 1996 interest. The problems it raises haven't been solved or even substantially reformulated.

Stephen Laurence and Eric Margolis have recently argued that certain kinds of regress arguments against the language of thought (LOT) hypothesis as an account of how we understand natural languages have been answered incorrectly or inadequately by supporters of LOT ('Regress arguments against the language of thought', Analysis, 57 (1), 60-6, J 97). They argue further that this does not undermine the LOT hypothesis, since the main sources of support for LOT are (or might be) independent of (...) it providing an account of how we understand natural language. In my paper I seek to refute both these claims, and reinstate the putative explanation of natural language understanding as a necessarily central part of the support for LOT. The main argument exploits the fact that Laurence and Margolis give too little weight to the ideas (a) that LOT might be innate (b) that for LOT supporters a semantic theory must apply to in-the-head tokens, not linguistic utterances. (shrink)

��We describe a comprehensive framework for text un- derstanding, based on the representation of context. It is designed to serve as a representation of semantics for the full range of in- terpretive and inferential needs of general natural language pro- cessing. Its most distinctive feature is its uniform representation of the various simple and independent linguistic sources that play a role in determining meaning: lexical associations, syntactic re- strictions, case-role expectations, and most importantly, contextual effects. Compositional syntactic structure (...) from a shallow parsing is represented in a neural net-based associative memory, where it then interacts through a Bayesian network with semantic associa- tions and the context or “gist” of the passage carried forward from preceding sentences. Experiments with more than 2000 sentences in different languages are included. (shrink)

This paper is concerned with those elements of planning knowledge that are common to both understanding someone else's plan and creating a plan for one's own use. This planning knowledge can be divided into two bodies: Knowledge about the world, and knowledge about the planning process itself. Our interest here is primarily with the latter corpus. The central thesis is that much of the knowledge about the planning process itself can be formulated in terms of higher‐level goals and plans (...) called meta‐goals and meta‐plans. These entities can then be used by the same understanding and planning mechanisms that process ordinary goals and plans. However, the meta‐planning knowledge now enables these mechanisms to handle much more complicated situations in a uniform manner.Systems based on meta‐planning would have a number of advantages over existing problem solving and understanding systems. The same knowledge could be shared by both a planner and understander, and both would be able to handle complex situations elegantly. In addition, in planning, the use of meta‐planning has several advantages over more traditional methods involving constraints or critics. Meta‐planning allows the full power of a problem solver to be applied to situations that are generally amenable only to special purpose processing. In addition, meta‐planning facilitates the representation of some situations that are difficult to express otherwise. We have begun to introduce meta‐planning knowledge into two systems: PAM, a story understanding program, and PANDORA, a problem solving and plannin̈g system. (shrink)

I advocate a theory of syntactic semantics as a way of understanding how computers can think (and how the Chinese-Room-Argument objection to the Turing Test can be overcome): (1) Semantics, considered as the study of relations between symbols and meanings, can be turned into syntax – a study of relations among symbols (including meanings) – and hence syntax (i.e., symbol manipulation) can suffice for the semantical enterprise (contra Searle). (2) Semantics, considered as the process of understanding one domain (...) (by modeling it) in terms of another, can be viewed recursively: The base case of semantic understanding –understanding a domain in terms of itself – is syntactic understanding. (3) An internal (or narrow ), first-person point of view makes an external (or wide ), third-person point of view otiose for purposes of understanding cognition. (shrink)

This paper describes the layers of context leveraged by language-endowed intelligent agents (LEIAs) during incremental natural language understanding (NLU). Context is defined as a combination of (...

The present paper intends to draw the conception of language implied in the technique of word embeddings that supported the recent development of deep neural network models in computational linguistics. After a preliminary presentation of the basic functioning of elementary artificial neural networks, we introduce the motivations and capabilities of word embeddings through one of its pioneering models, word2vec. To assess the remarkable results of the latter, we inspect the nature of its underlying mechanisms, which have been characterized as (...) the implicit factorization of a word-context matrix. We then discuss the ordinary association of the “distributional hypothesis” with a “use theory of meaning,” often justifying the theoretical basis of word embeddings, and contrast them to the theory of meaning stemming from those mechanisms through the lens of matrix models (such as vector space models and distributional semantic models). Finally, we trace back the principles of their possible consistency through Harris’s original distributionalism up to the structuralist conception of language of Saussure and Hjelmslev. Other than giving access to the technical literature and state of the art in the field of natural language processing to non-specialist readers, the paper seeks to reveal the conceptual and philosophical stakes involved in the recent application of new neural network techniques to the computational treatment of language. (shrink)

This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves — rather than with their mathematical representations (as (...) in symbolic models). This paper uses the natural-language ABMA to test the robustness of symbolic reason-balancing models of argumentation (Mäs & Flache 2013; Singer et al. 2019): First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from symbolic models. However, once ADAs start to actively generate new contributions, the evolution of a conversation is dominated by properties of the agents as authors. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research. (shrink)

In this paper a logical interpretation of semantic nets and graph grammars is proposed for modelling natural language understanding and creating language understanding computer systems. An example of parsing a Finnish question by graph grammars and inferring the answer to it by a semantic net representation is provided.

This paper explores two important ways in which the practice of close reading differs from the technique of natural language processing, the use of computer programming to decode, process, and replicate messages within a human language. It does so in order to highlight distinctive features of close reading that are not replicated by natural language processing. The first point of distinction concerns the nature of the meaning generated in each case. While natural language (...) processing proceeds on the principle that a text’s meaning can be deciphered by applying the rules governing the language in which the text is written, close reading is premised on the idea that this meaning lies in the interplay that the text prompts within readers. While the semantic theory of meaning upon which natural language processing programs are based is often taken for granted today, I draw from phenomenological and hermeneutic theories, particularly Wolfgang Iser and Hans-Georg Gadamer, to explain why a different theory of meaning is necessary for understanding the meaning generated by close reading. Second, while natural language processing programs are considered successful when they generate what epistemologists call true beliefs about a text, I argue that close reading aims first and foremost at the development, not of true belief, but of understanding. To develop this distinction, I draw from recent scholarship on the epistemology of education, including work by Duncan Pritchard, to explain how understanding differs from true belief and why attainment of the latter is less educationally significant than the former. (shrink)

Meaning depends on context. This applies in obvious cases like deictics or sarcasm as well as more subtle situations like framing or persuasion. One key aspect of this is the identity of the participants in an interaction. Our interpretation of an utterance shifts based on a variety of factors, including personal history, background knowledge, and our relationship to the source. While obviously an incomplete model of individual differences, demographic factors provide a useful starting point and allow us to capture some (...) of this variance. However, the relevance of specific demographic factors varies between situations—where age might be the key factor in one context, ideology might dominate in another. To address this challenge, we introduce a method for combining demographics and context into situated demographic embeddings—mapping representations into a continuous geometric space appropriate for the given domain, showing the resulting representations to be functional and interpretable. We further demonstrate how to make use of related external data so as to apply this approach in low‐resource situations. Finally, we show how these representations can be incorporated into improve modeling of real‐world natural language understanding tasks, improving model performance and helping with issues of data sparsity. (shrink)

We introduce a computational theory of situated language understanding in which the meaning of words and utterances depends on the physical environment and the goals and plans of communication partners. According to the theory, concepts that ground linguistic meaning are neither internal nor external to language users, but instead span the objective‐subjective boundary. To model the possible interactions between subject and object, the theory relies on the notion of perceived affordances: structured units of interaction that can be (...) used for prediction at multiple levels of abstraction. Language understanding is treated as a process of filtering perceived affordances. The theory accounts for many aspects of the situated nature of human language use and provides a unified solution to a number of demands on any theory of language understanding including conceptual combination, prototypicality effects, and the generative nature of lexical items.To support the theory, we describe an implemented system that understands verbal commands situated in a virtual gaming environment. The implementation uses probabilistic hierarchical plan recognition to generate perceived affordances. The system has been evaluated on its ability to correctly interpret free‐form spontaneous verbal commands recorded from unrehearsed game play between human players. The system is able to “step into the shoes” of human players and correctly respond to a broad range of verbal commands in which linguistic meaning depends on social and physical context. We quantitatively compare the system's predictions in response to direct player commands with the actions taken by human players and show generalization to unseen data across a range of situations and verbal constructions. (shrink)

This paper describes a prototype implementation of a Labelled Deduction System for natural language interpretation, where interpretation is taken to be the process of understanding a natural language utterance. The implementation models the process of understanding wh-gap dependencies in questions and relative clauses for a fragment of English. The paper is divided in three main sections. In Section 1, we introduce the basic architecture of the system. Section 2 outlines a prototype implementation of wh-binding (...) and indicates its potential for explanation of linguistic phenomena, and in Section 3 we briefly set the model within a large theoretical perspective, comparing it to other type-logical approaches to natural language analysis. (shrink)

This book develops a theory of enriched meanings for natural language interpretation that uses the concept of monads and related ideas from category theory. The volume is interdisciplinary in nature, and will appeal to graduate students and researchers from a range of disciplines interested in natural language understanding and representation.

Natural language processing is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are (...) applied, including document term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals. (shrink)

As modern deep networks become more complex, and get closer to human‐like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity (...) in representations and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances—similar to human zero‐shot reasoning. However, we also note some shortcomings in this generalization behavior—similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human‐like language understanding. (shrink)

Most theories of semantic competence in natural language implicitly assume the Language of Thought Hypothesis. According to this hypothesis, all human cognition consists in the deployment of a language of thought. This language of thought is supposed to be independent of natural language, yet at the same time, it is supposed to be semantically isomorphic with natural language. Given this assumption, it is easy to answer basic questions regarding semantic competence in (...) natural language. What are semantic properties of natural language, and how did they originate? Natural language inherits the semantic properties of the language of thought it is used to express. What does semantic competence consist in? It consists in knowing how to translate items in one's language of thought into a public medium. ;The starting point of this dissertation is the rejection, by some prominent paradigms in cognitive science, of the view that human cognition consists in the deployment of a language of thought that is, at the same time, independent of natural language, yet semantically isomorphic with it. The dissertation constitutes a fledgling attempt to explore an alternative approach to explaining semantic competence in natural language, an approach that is compatible with this rejection of the Language of Thought Hypothesis. ;Rather than conceive of natural language as a tool for expressing items in a pre-existing, semantically isomorphic, language of thought, I urge that language be conceived of as a norm-governed social practice. On this view, the semantic properties of natural language are determined by the inferential norms governing linguistic performances. In order to explain the origin of its semantic properties, one must explain the origin of the inferential norms that govern language. In order to explain what semantic competence consists in, one must explain how individuals learn to abide by these inferential norms. The bulk of the dissertation is devoted to showing that neither of these explanatory projects requires endorsing the Language of Thought Hypothesis. I do this by sketching the form that such explanations might take, and showing that they are consistent with much current research in ethology, comparative psychology, developmental psychology, and evolutionary biology. (shrink)

Natural language processing (NLP) has undergone significant changes in its methods, reflecting advances in computing technology and cognitive research. This article reviews the key stages of the evolution of natural language processing methods. The article touches on the topic of the first NLP systems developed, provides justification for the reasons for the complexity of some processed texts and the possible depth of analysis. In addition, it describes not only NLP methods before and after the GPT revolution, (...) but also current trends and prospects in the field of natural language processing. The article allows us to trace how the idea of natural language text has changed during the development of computer analysis methods, as well as to understand what text is in the mirror of natural language processing, what is really the subject of natural language processing research and what cannot be seen through the eyes of a simple researcher who does not use NLP methods. (shrink)

While cognitive linguists have been successful at providing accounts of the stable knowledge structures (conceptual metaphors) that give rise to figurative language, and the conceptual mechanisms that manipulate these knowledge structures (conceptual blending), relatively less effort has been thus far devoted to the nature of the linguistic mechanisms involved in figurative language understanding. This paper presents a theoretical account of figurative language understanding, examining metaphor and metonymy in particular. This account is situated within the Theory (...) of Lexical Concepts and Cognitive Models (LCCM Theory). LCCM Theory (Evans, Cognitive Linguistics 17: 491–534, 2006, How words mean: Lexical concepts, cognitive models and meaning construction, Oxford University Press, 2009b) is a cognitively realistic model of lexical representation and semantic compositionality, providing, it is argued, an account of figurative language which complements the ‘backstage’ cognition perspective of Conceptual Blending Theory. It also integrates the notion of conceptual metaphor within the account provided of figurative language understanding. The paper introduces the key mechanisms involved in figurative language understanding arising from language use. The paper also provides a programmatic account of how conceptual metaphors are integrated with linguistic knowledge in figurative language use. It is argued the present proposals flesh out a key aspect of the conceptual integration perspective promoted by Fauconnier and Turner, with which LCCM Theory is continuous. In part, the paper attempts to advance the prospect of a ‘joined up’ cognitive linguistic account of figurative language understanding. (shrink)

In this paper, we present a model of pathos, delineate its operationalisation, and demonstrate its utility through an analysis of natural language argumentation. We understand pathos as an interactional persuasive process in which speakers are performing pathos appeals and the audience experiences emotional reactions. We analyse two strategies of such appeals in pre-election debates: pathotic Argument Schemes based on the taxonomy proposed by Walton et al. (Argumentation schemes, Cambridge University Press, Cambridge, 2008), and emotion-eliciting language based on (...) psychological lexicons of emotive words (Wierzba in Behav Res Methods 54:2146–2161, 2021). In order to match the appeals with possible reactions, we collect real-time social media reactions to the debates and apply sentiment analysis (Alswaidan and Menai in Knowl Inf Syst 62:2937–2987, 2020) method to observe emotion expressed in language. The results point to the importance of pathos analysis in modern discourse: speakers in political debates refer to emotions in most of their arguments, and the audience in social media reacts to those appeals using emotion-expressing language. Our results show that pathos is a common strategy in natural language argumentation which can be analysed with the support of computational methods. (shrink)

A new comprehensive framework for narrative understanding has been developed. Its centerpiece is a new situational logic calledEpisodic Logic, a knowledge and semantic representation well-adapted to the interpretive and inferential needs of general NLU. The most distinctive features of EL is its natural language-like expressiveness. It allows for generalized quantifiers, lambda abstraction, sentence and predicate modifiers, sentence and predicate reification, intensional predicates, unreliable generalizations, and perhaps most importantly, explicit situational variables linked to arbitrary formulas that describe them. (...) These allow episodes to be explicitly related in terms of part-whole, temporal and causal relations. Episodic logical form is easily computed from surface syntax and lends itself to effective inference. (shrink)

This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information and data-processing systems of all kinds, no matter whether human, (other) animal or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and phi losophical psychology through issues in cognitive psychology and socio biology (concerning the mental capabilities of other species) to ideas related to artificial intelligence and computer science. While primary (...) emphasis will be placed upon theoretical, conceptual and epistemologi cal aspects of these problems and domains, empirical, experimental and methodological studies will also appear from time to time. Among the most challenging and difficult projects within the scope of artificial intelligence is the development and implementation of com puter programs suitable for processing natural language. Our purpose in compiling the present volume has been to contribute to the foundations of this enterprise by bringing together classic papers devoted to crucial problems involved in understanding natural language, which range from issues of formal syntax and logical form to those of possible-worlds and situation semantics. The book begins with a comprehensive introduc tion composed by Jack Kulas, the senior editor of this work, which pro vides a systematic orientation to this complex field, and ends with a selected bibliography intended to promote further research. If our efforts assist others in dealing with these problems, they will have been worthwhile. J. H. F. (shrink)

The natural epistemology of W. V. Quine has not been well understood. Critics argue that Quine's scientific approach to epistemology is circular and fails to be normative, yet these criticisms tend to be based on the very presuppositions concerning language, theory, and epistemology that Quine is at pains to reject or alter. ;Quine's views on the meaningfulness of language use imply a breakdown in the dichotomy between language as a theoretically neutral instrument and theory as the (...) commitment to some subset of the sentences allowed by a language. This breakdown, and the view of language which drives it, result in Quine's doctrine that speaking a language involves substantial theoretical commitments, and that inquiry must begin from within some ongoing theory of the world. That is, our epistemic practices, including epistemology itself, must be globally circular. The traditionally fundamental norm of linear propositional support, which drives the circularity objection, is thus seen as based in a misconception of language and theory. This not only explains Quine's lack of concern with the circularity of his view, but also illuminates the fundamental motivation for his general naturalism. ;Natural epistemology cannot be normative in the traditional sense of providing an independent ground for science. However, having accepted the "terminal parameter" of prediction as the test of theory, normativity becomes a matter of describing which procedures best promote predictively successful theory. Thus, epistemic norms emerge from science as hypotheses concerning the effectiveness of various methods in promoting predictively successful theory. Not only can natural epistemology prescribe and proscribe various epistemic practices, it offers the promise of an explanatory understanding of the strengths and weaknesses of those practices. Based on this understanding, natural epistemology further involves the improvement of current practices and the development of new ones. Crucial to this reconception of epistemology is the reconception of the knower as an adaptive organism employing the language/theory complex as an adaptive tool for interacting with the environment. ;The naturalism/anti-naturalism debate is advanced by acknowledging the substantial theoretical commitments implicit in the traditional view, and by examining Quine's reconception of language, theory, and the human knowing subject. (shrink)

Machine learning and statistical methods have yielded impressive results in a wide variety of natural language processing tasks. These advances have generally been regarded as engineering achievements. In fact it is possible to argue that the success of machine learning methods is significant for our understanding of the cognitive basis of language acquisition and processing. Recent work in unsupervised grammar induction is particularly relevant to this issue. It suggests that knowledge of language can be achieved (...) through general learning procedures, and that a richly articulated language faculty is not required to explain its acquisition. (shrink)

Young children's early understanding of emotion was investigated by examining their use of emotion terms such as happy, sad, mud, and cry. Five children's emotion language was examined longitudinally from the age of 2 to 5 years, and as a comparison their reference to pains via such terms as burn, sting, and hurt was also examined. In Phase 1 we confirmed and extended prior findings demonstrating that by 2 years of age terms for the basic emotions of happiness, (...) sadness, anger, and fear are commonly used by children as are terms for such related states as crying and hurting. At this early age children produce such terms to refer to self and to others, and to past and future as well as to present states. Over the years from 2 to 5 children's emotion vocabulary expands, their discussion of hypothetical emotions gets underway, and the complexity of their emotion utterances increases. In Phase 2 our analyses go beyond children's production of emotion terms to analyses of their conception of emotion. We focus especially on when children use emotion terms to refer to subjective experiential states of persons. From their earliest uses of these terms in our data children. (shrink)

Many scholars often use the terms “metaphors,” “analogies,” and “models” interchangeably and inadvertently overlook the uniqueness of each word. According to recent cognitive studies, the three terms involve distinct cognitive processes using features from a familiar concept and applying them to an abstract, complicated concept. In the field of science and religion, there have been various objects or ideas used as metaphors, analogies, or models to describe the science–religion relationship. Although these heuristic tools provided some understanding of the complex (...) interaction, they failed to address the broad nature of science and religion as well as the multifarious relationship between the two in a sociocultural context. Unlike the previous candidates, the concept of language, including the notions of linguistic worldview, linguistic identity, dialects, power, and bilingualism, offers a unique and comprehensive window through which science, religion, and the relationship between the two are seen with clarity. (shrink)

A proposal is made concerning the introduction of the notions of cause and reason into a natural language understanding system. Its hypothesis is that one should prefer rational explanations of actions when dealing with human, or human‐like, agents, if one can find them in what one is analyzing, but that in other, nonhuman, cases one should prefer causal explanations. The reader is reminded of the existing state of the preference semantics system, and then are described the changes (...) that would have to be made to incorporate the above proposal and to find examples to test it. Finally, it is argued that there is no reason to think that a more complex taxonomy of causes (beyond the opposition of causes and reasons) is required, at least not if, as here. one seeks a procedural distinction corresponding to any taxonomic distinction among inference rules. (shrink)

This book presents a new theory of how children acquire language and discusses its implications for a wide range of topics. It explores the roles of innateness and experience in language acquisition, provides further evidence for the theory of Universal Grammar, and shows how linguistic development in children is a driving force behind language shifts and changes.Charles Yang surveys a wide range of errors in children's language and identifies overlooked patterns. He combines these with work in (...) biological evolution in order to develop a model of language acquisition by which to understand the interaction between children's internal linguistic knowledge and their external linguistic experience. He then presents evidence from his own and others' research in the acquisition of syntax and morphology and data from historical language change to test its validity. The model is the first to make quantitative and cross-linguistic predictions about child language. It may also be deployed as a predictive model of language change which, when the evidence is available, could explain why grammars change in a particular direction at a particular time.Knowledge and Learning in Natural Language is a pioneering work at the centre of current concerns in linguistics and cognitive science. It will interest all those concerned to understand and explain language acquisition, Universal Grammar, and language change. (shrink)

The origins of these essays -- Introduction -- Presupposition -- A projection problem for speaker presupposition -- Language and linguistic competence -- Linguistics and psychology -- Semantics and psychology -- Semantics and semantic competence -- The necessity argument -- Truth, meaning, and understanding -- Truth and meaning in perspective -- Semantics and pragmatics -- Naming and asserting -- The gap between meaning and assertion : why what we literally say often differs from what our words literally mean -- (...) Drawing the line between meaning and implicaturem and relating both to assertion -- Descriptions -- Incomplete definite descriptions -- Donnellan's referential/attributive distinction -- Why incomplete descriptions don't refute Russell's theory of descriptions -- Meaning and use : lessons for legal interpretation -- Interpreting legal texts : what is and what is not special about the law. (shrink)