Results for 'Natural Language Generation'

965 found
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  1. Natural language generation in healthcare.Alison Cawsey - unknown
    Good communication is vital in healthcare both among healthcare professionals and be tween healthcare professionals and their patients And well written documents describing and or explaining the information in structured databases may be easier to comprehend more edifying and even more convincing than the structured data even when presented in tabu lar or graphic form Documents may be automatically generated from structured data using techniques from the eld of natural language generation These techniques are concerned with how (...)
     
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  2.  46
    Natural language generation of biomedical argumentation for lay audiences.Nancy Green, Rachael Dwight, Kanyamas Navoraphan & Brian Stadler - 2011 - Argument and Computation 2 (1):23 - 50.
    This article presents an architecture for natural language generation of biomedical argumentation. The goal is to reconstruct the normative arguments that a domain expert would provide, in a manner that is transparent to a lay audience. Transparency means that an argument's structure and functional components are accessible to its audience. Transparency is necessary before an audience can fully comprehend, evaluate or challenge an argument, or re-evaluate it in light of new findings about the case or changes in (...)
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  3.  7
    Pragmatics and natural language generation.Eduard H. Hovy - 1990 - Artificial Intelligence 43 (2):153-197.
  4. Declarative programming for natural language generation.Matthew Stone - manuscript
    Algorithms for NLG NLG is typically broken down into stages of discourse planning (to select information and organize it into coherent paragraphs), sentence planning (to choose words and structures to fit information into sentence-sized units), and realization (to determine surface form of output, including word order, morphology and final formatting or intonation). The SPUD system combines the generation steps of sentence planning and surface realization by using a lexicalized grammar to construct the syntax and semantics of a sentence simultaneously.
     
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  5.  8
    Knowledge-intensive natural language generation.Paul S. Jacobs - 1987 - Artificial Intelligence 33 (3):325-378.
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  6.  8
    Interleaving natural language parsing and generation through uniform processing.Günter Neumann - 1998 - Artificial Intelligence 99 (1):121-163.
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  7.  44
    Avicenna: a challenge dataset for natural language generation toward commonsense syllogistic reasoning.Zeinab Aghahadi & Alireza Talebpour - 2022 - Journal of Applied Non-Classical Logics 32 (1):55-71.
    Syllogism is a type of everyday reasoning. For instance, given that ‘Avicenna wrote the famous book the Canon of Medicine’ and ‘The Canon of Medicine has influenced modern medicine,’ it can be conc...
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  8.  24
    Relating Mori’s Uncanny Valley in generating conversations with artificial affective communication and natural language processing.Feni Betriana, Kyoko Osaka, Kazuyuki Matsumoto, Tetsuya Tanioka & Rozzano C. Locsin - 2021 - Nursing Philosophy 22 (2):e12322.
    Human beings express affinity (Shinwa‐kan in Japanese language) in communicating transactive engagements among healthcare providers, patients and healthcare robots. The appearance of healthcare robots and their language capabilities often feature characteristic and appropriate compassionate dialogical functions in human–robot interactions. Elements of healthcare robot configurations comprising its physiognomy and communication properties are founded on the positivist philosophical perspective of being the summation of composite parts, thereby mimicking human persons. This article reviews Mori's theory of the Uncanny Valley and its (...)
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  9. Natural languages and context-free languages.Geoffrey K. Pullum & Gerald Gazdar - 1980 - Linguistics and Philosophy 4 (4):471 - 504.
    Notice that this paper has not claimed that all natural languages are CFL's. What it has shown is that every published argument purporting to demonstrate the non-context-freeness of some natural language is invalid, either formally or empirically or both.18 Whether non-context-free characteristics can be found in the stringset of some natural language remains an open question, just as it was a quarter century ago.Whether the question is ultimately answered in the negative or the affirmative, there (...)
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  10.  70
    Natural language processing for transparent communication between public administration and citizens.Bernardo Magnini, Elena Not, Oliviero Stock & Carlo Strapparava - 2000 - Artificial Intelligence and Law 8 (1):1-34.
    This paper presents two projects concerned with the application of natural language processing technology for improving communication between Public Administration and citizens. The first project, GIST,is concerned with automatic multilingual generation of instructional texts for form-filling. The second project, TAMIC, aims at providing an interface for interactive access to information, centered on natural language processing and supposed to be used by the clerk but with the active participation of the citizen.
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  11. Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics.Gregor Betz - 2022 - JASSS 25 (1).
    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 (...)
     
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  12. Natural language, sortal reducibility and generalized quantifiers.Edward L. Keenan - 1993 - Journal of Symbolic Logic 58 (1):314-325.
    Recent work in natural language semantics leads to some new observations on generalized quantifiers. In § 1 we show that English quantifiers of type $ $ are booleanly generated by their generalized universal and generalized existential members. These two classes also constitute the sortally reducible members of this type. Section 2 presents our main result--the Generalized Prefix Theorem (GPT). This theorem characterizes the conditions under which formulas of the form Q1x 1⋯ Qnx nRx 1⋯ xn and q1x 1⋯ (...)
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  13.  11
    Plan-based integration of natural language and graphics generation.Wolfgang Wahlster, Elisabeth André, Wolfgang Finkler, Hans-Jürgen Profitlich & Thomas Rist - 1993 - Artificial Intelligence 63 (1-2):387-427.
  14.  12
    Natural language syntax complies with the free-energy principle.Elliot Murphy, Emma Holmes & Karl Friston - 2024 - Synthese 203 (5):1-35.
    Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design—such as “minimal search” criteria (...)
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  15. (1 other version)Dynamic Context Generation for Natural Language Understanding: A Multifaceted Knowledge Approach.Samuel W. K. Chan - unknown
    ��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 (...)
     
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  16.  74
    Why Natural Language Processing is Not Reading: Two Philosophical Distinctions and their Educational Import.Carolyn Culbertson - 2025 - Journal of Applied Hermeneutics 2025.
    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 (...)
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  17.  23
    Utility and Language Generation: The Case of Vagueness.Kees Deemter - 2009 - Journal of Philosophical Logic 38 (6):607-632.
    This paper asks why information should ever be expressed vaguely, re-assessing some previously proposed answers to this question and suggesting some new ones. Particular attention is paid to the benefits that vague expressions can have in situations where agreement over the meaning of an expression cannot be taken for granted. A distinction between two different versions of the above-mentioned question is advocated. The first asks why human languages contain vague expressions, the second question asks when and why a speaker should (...)
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  18.  40
    Using Neural Networks to Generate Inferential Roles for Natural Language.Peter Blouw & Chris Eliasmith - 2018 - Frontiers in Psychology 8:295741.
    Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences (...)
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  19.  67
    Game Theory and Language Generation.Kees van Deemter - unknown
    This informal position paper brings together some recent developments in formal semantics and pragmatics to argue that the discipline of Game Theory is well placed to become the theoretical backbone of Natural Language Generation. To demonstrate some of the strengths and weaknesses of the Game-Theoretical approach, we focus on the utility of vague expressions. More specifically, we ask what light Game Theory can shed on the question when an NLG system should generate vague language.
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  20.  30
    Generating Use Case Models from Arabic User Requirements in a Semiautomated Approach Using a Natural Language Processing Tool.Sari Jabbarin & Nabil Arman - 2015 - Journal of Intelligent Systems 24 (2):277-286.
    Automated software engineering has attracted a large amount of research efforts. The use of object-oriented methods for software systems development has made it necessary to develop approaches that automate the construction of different Unified Modeling Language models in a semiautomated approach from textual user requirements. UML use case models represent an essential artifact that provides a perspective of the system under analysis or development. The development of such use case models is very crucial in an object-oriented development method. The (...)
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  21.  12
    Discourse strategies for generating natural-language text.Kathleen R. McKeown - 1985 - Artificial Intelligence 27 (1):1-41.
  22. Utility and Language Generation: The Case of Vagueness.Kees van Deemter - 2009 - Journal of Philosophical Logic 38 (6):607 - 632.
    This paper asks why information should ever be expressed vaguely, re-assessing some previously proposed answers to this question and suggesting some new ones. Particular attention is paid to the benefits that vague expressions can have in situations where agreement over the meaning of an expression cannot be taken for granted. A distinction between two different versions of the above-mentioned question is advocated. The first asks why human languages contain vague expressions, the second question asks when and why a speaker should (...)
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  23. Some comments on the begriffsschrift and natural language.Alan Schwerin - 1983 - Philosophical Papers 12 (2):32-38.
    If the begriffsschrift from Frege does represent the logical form of natural language it either lacks a logical form itself, or its logical form is different to that of natural language. But Frege insists that his notation has a logical form. So the second disjunct holds. This suggests that Frege's notation will generate consequences different to those that can be derived with natural language, with its different logical form. For anyone looking for "a means (...)
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  24. On the very idea of a theory of meaning for a natural language.Eugen Fischer - 1997 - Synthese 111 (1):1-8.
    A certain orthodoxy has it that understanding is essentially computational: that information about what a sentence means is something that may be generated by means of a derivational process from information about the significance of the sentences constituent parts and of the ways in which they are put together. And that it is therefore fruitful to study formal theories acceptable as compositional theories of meaning for natural languages: theories that deliver for each sentence of their object-language a theorem (...)
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  25.  18
    On the Nature of Explanation: An Epistemological-Linguistic Perspective for Explanation-Based Natural Language Inference.Marco Valentino & André Freitas - 2024 - Philosophy and Technology 37 (3):1-33.
    One of the fundamental research goals for explanation-based Natural Language Inference (NLI) is to build models that can reason in complex domains through the generation of natural language explanations. However, the methodologies to design and evaluate explanation-based inference models are still poorly informed by theoretical accounts on the nature of explanation. As an attempt to provide an epistemologically grounded characterisation for NLI, this paper focuses on the scientific domain, aiming to bridge the gap between theory (...)
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  26.  22
    At the intersection of humanity and technology: a technofeminist intersectional critical discourse analysis of gender and race biases in the natural language processing model GPT-3.M. A. Palacios Barea, D. Boeren & J. F. Ferreira Goncalves - forthcoming - AI and Society:1-19.
    Algorithmic biases, or algorithmic unfairness, have been a topic of public and scientific scrutiny for the past years, as increasing evidence suggests the pervasive assimilation of human cognitive biases and stereotypes in such systems. This research is specifically concerned with analyzing the presence of discursive biases in the text generated by GPT-3, an NLPM which has been praised in recent years for resembling human language so closely that it is becoming difficult to differentiate between the human and the algorithm. (...)
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  27.  2
    Working on the argument pipeline: Through flow issues between natural language argument, instantiated arguments, and argumentation frameworks.Floriana Grasso, Floris Bex & Nancy Green - 2016 - Argument and Computation 7 (1):69-89.
    In many domains of public discourse such as arguments about public policy, there is an abundance of knowledge to store, query, and reason with. To use this knowledge, we must address two key general problems: first, the problem of the knowledge acquisition bottleneck between forms in which the knowledge is usually expressed, e.g., natural language, and forms which can be automatically processed; second, reasoning with the uncertainties and inconsistencies of the knowledge. Given such complexities, it is labour and (...)
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  28.  25
    Semantics of Natural Language[REVIEW]L. J. - 1973 - Review of Metaphysics 26 (3):531-533.
    J. L. Austin, in "Ifs and Cans," proclaimed the common hope that we soon "may see the birth, through the joint labors of philosophers, grammarians, and numerous other students of language, of a true and comprehensive science of language." The problem has always been with the "joint labors" part. Philosophers have always been willing to issue linguists dictums and linguists have been happy to teach philosophers "plain facts." Austin’s general view of language, and his particular notion of (...)
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  29.  38
    Meaning, Form and the Limits of Natural Language Processing.Jan Segessenmann, Jan Juhani Steinmann & Oliver Dürr - 2023 - Philosophy, Theology and the Sciences 10 (1):42-72.
    This article engages the anthropological assumptions underlying the apprehensions and promises associated with language in artificial intelligence (AI). First, we present the contours of two rivalling paradigms for assessing artificial language generation: a holistic-enactivist theory of language and an informational theory of language. We then introduce two language generation models – one presently in use and one more speculative: Firstly, the transformer architecture as used in current large language models, such as the (...)
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  30.  37
    Mastering a Natural Language: Rationalists Versus Empiricists.Joseph Margolis - 1973 - Diogenes 21 (84):41-57.
    Behaviorist theories of language acquisition are the most prominent among current empiricist theories of language. But the inherent weaknesses of behaviorism—whether or not applied to language acquisition or linguistic meaning or the like—do not as such call into question the adequacy of the empiricist conception of language. The issue is central to contemporary speculation about the nature of linguistic competence and the infant's acquisition of language. Empiricism has, in fact, been vigorously challenged in the most (...)
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  31. Darmok and Jalad on the Internet: the importance of metaphors in natural languages and natural language processing.Kristina Šekrst - 2023 - In Amy H. Sturgis & Emily Strand (eds.), Star Trek: Essays Exploring the Final Frontier. Vernon Press. pp. 89-117.
    In a Star Trek: The Next Generation episode, Cpt. Picard is captured and trapped on a planet with an alien captain who speaks a language incompatible with the universal translator, based on their societal historical metaphors. According to Shapiro (2004), the concept of a universal translator removes everything alien from alien languages, and since the Tamarian language refers only to their historical and cultural archetypes, Picard can only establish dialogue by invoking human analogues, such as Gilgamesh. The (...)
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  32.  11
    Evaluating large language models’ ability to generate interpretive arguments.Zaid Marji & John Licato - 2024 - Argument and Computation:1-51.
    In natural language understanding, a crucial goal is correctly interpreting open-textured phrases. In practice, disagreements over the meanings of open-textured phrases are often resolved through the generation and evaluation of interpretive arguments, arguments designed to support or attack a specific interpretation of an expression within a document. In this paper, we discuss some of our work towards the goal of automatically generating and evaluating interpretive arguments. We have curated a set of rules from the code of ethics (...)
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  33. Generation of Referring Expressions: Assessing the Incremental Algorithm.Kees van Deemter, Albert Gatt, Ielka van der Sluis & Richard Power - 2012 - Cognitive Science 36 (5):799-836.
    A substantial amount of recent work in natural language generation has focused on the generation of ‘‘one-shot’’ referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms. (...)
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  34.  49
    On the Rules of Consequence for a Natural Language.Henry Hiż - 1973 - The Monist 57 (3):312-327.
    A large part of philosophical analysis deals with the problem of how language relates to reality. It is assumed that language speaks—or at least may speak—about something, about one or another kind of reality. It speaks truthfully or falsely, with cohesion or with confusion, with precision or loosely, penetratingly or without much depth, assertively, hypothetically, optatively, interrogatively or in some other way, about the real or supposed world. The so-called “real world” is perhaps amorphous, in flux, continuous and (...)
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  35. Managing Ambiguity in Reference Generation: The Role of Surface Structure.Imtiaz H. Khan, Kees van Deemter & Graeme Ritchie - 2012 - Topics in Cognitive Science 4 (2):211-231.
    This article explores the role of surface ambiguities in referring expressions, and how the risk of such ambiguities should be taken into account by an algorithm that generates referring expressions, if these expressions are to be optimally effective for a hearer. We focus on the ambiguities that arise when adjectives occur in coordinated structures. The central idea is to use statistical information about lexical co-occurrence to estimate which interpretation of a phrase is most likely for human readers, and to avoid (...)
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  36.  47
    Generating coherence relations via internal argumentation.Rodger Kibble - 2007 - Journal of Logic, Language and Information 16 (4):387-402.
    A key requirement for the automatic generation of argumentative or explanatory text is to present the constituent propositions in an order that readers will find coherent and natural, to increase the likelihood that they will understand and accept the author’s claims. Natural language generation systems have standardly employed a repertoire of coherence relations such as those defined by Mann and Thompson’s Rhetorical Structure Theory. This paper models the generation of persuasive monologue as the outcome (...)
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  37.  41
    Computational Interpretations of the Gricean Maxims in the Generation of Referring Expressions.Robert Dale & Ehud Reiter - 1995 - Cognitive Science 19 (2):233-263.
    We examine the problem of generating definite noun phrases that are appropriate referring expressions; that is, noun phrases that (a) successfully identify the intended referent to the hearer whilst (b) not conveying to him or her any false conversational implicatures (Grice, 1975). We review several possible computational interpretations of the conversational implicature maxims, with different computational costs, and argue that the simplest may be the best, because it seems to be closest to what human speakers do. We describe our recommended (...)
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  38.  87
    On pitfalls (and advantages) of sophisticated Large Language Models.Anna Strasser - 2024 - In Joan Casas-Roma, Santi Caballe & Jordi Conesa (eds.), Ethics in Online AI-Based Systems: Risks and Opportunities in Current Technological Trends. Academic Press.
    Natural language processing based on large language models (LLMs) is a booming field of AI research. After neural networks have proven to outperform humans in games and practical domains based on pattern recognition, we might stand now at a road junction where artificial entities might eventually enter the realm of human communication. However, this comes with serious risks. Due to the inherent limitations regarding the reliability of neural networks, overreliance on LLMs can have disruptive consequences. Since it (...)
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  39.  5
    Validating Silent Gesture Lab Studies in a Naturally Emerging Sign Language: How Order is Used to Describe Intensional Versus Extensional Events in Nicaraguan Sign Language.Molly Flaherty & Marieke Schouwstra - forthcoming - Topics in Cognitive Science.
    Languages are neither designed in classrooms nor drawn from dictionaries—they are products of human minds and human interactions. However, it is challenging to understand how structure grows in these circumstances because generations of use and transmission shape and reshape the structure of the languages themselves. Laboratory studies on language emergence investigate the origins of language structure by requiring participants, prevented from using their own natural language(s), to create a novel communication system and then transmit it to (...)
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  40. Lexical choice and conceptual perspective in the generation of plural referring expressions.Albert Gatt & Kees van Deemter - 2007 - Journal of Logic, Language and Information 16 (4):423-443.
    A fundamental part of the process of referring to an entity is to categorise it (for instance, as the woman). Where multiple categorisations exist, this implicitly involves the adoption of a conceptual perspective. A challenge for the automatic Generation of Referring Expressions is to identify a set of referents coherently, adopting the same conceptual perspective. We describe and evaluate an algorithm to achieve this. The design of the algorithm is motivated by the results of psycholinguistic experiments.
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  41. Language as Signs.John Weldon Powell - 1988 - Dissertation, University of Oregon
    Philosophers disagree, with some rare exceptions. One of those exceptions is the broadest-brush account of what language is. Language is a system of signs used for the communication of --well, and here the agreement begins to break down--thoughts, ideas, messages, propositions or propositional contents, intentions, and a host of technical terms offer themselves to chink the cracks. A list of philosophers subscribing would be impossible to complete. Locke, Carnap, Augustine, Hobbes, Fodor, Katz, Chomsky, Derrida, --well, and on and (...)
     
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  42. Formal Language Theory and its Interdisciplinary Applications.Chia-Hua Lin - 2024 - In Tarja Knuuttila, Natalia Carrillo & Rami Koskinen (eds.), The Routledge Handbook of Philosophy of Scientific Modeling. New York, NY: Routledge.
    This chapter discusses the use of formal language theory in the investigation of diverse phenomena such as natural languages, computer code, and animal cognition. Formal language theory deals with mathematically defined languages as well as the formal systems, such as grammars and automata, that are used to define them. In this context, a language is a set of strings, a grammar specifies a set of rules for forming the string-set from an alphabet, and an automaton is (...)
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  43. Artificial Speech and Its Authors.Philip J. Nickel - 2013 - Minds and Machines 23 (4):489-502.
    Some of the systems used in natural language generation (NLG), a branch of applied computational linguistics, have the capacity to create or assemble somewhat original messages adapted to new contexts. In this paper, taking Bernard Williams’ account of assertion by machines as a starting point, I argue that NLG systems meet the criteria for being speech actants to a substantial degree. They are capable of authoring original messages, and can even simulate illocutionary force and speaker meaning. Background (...)
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  44. Fully generated scripted dialogue for embodied conversational agents'.Kees van Deemter, Brigitte Krenn, Paul Piwek, Marc Schroeder, Martin Klesen & Stefan Baumann - manuscript
    (Near-final version.) Accepted for publication in Artificial Intelligence Journal.
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  45. Underspecified Interpretations in a Curry-Typed Representation Language.Chris Fox & Shalom Lappin - 2005 - Journal of Logic and Computation 15 (2):131--143.
    In previous work we have developed Property Theory with Curry Typing (PTCT), an intensional first-order logic for natural language semantics. PTCT permits fine-grained specifications of meaning. It also supports polymorphic types and separation types. We develop an intensional number theory within PTCT in order to represent proportional generalized quantifiers like "most", and we suggest a dynamic type-theoretic approach to anaphora and ellipsis resolution. Here we extend the type system to include product types, and use these to define a (...)
     
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  46.  85
    Large Language Models Demonstrate the Potential of Statistical Learning in Language.Pablo Contreras Kallens, Ross Deans Kristensen-McLachlan & Morten H. Christiansen - 2023 - Cognitive Science 47 (3):e13256.
    To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language–especially those involving computational modeling–have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide (...)
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  47. Semantic Holism and Language Learning.Martin L. Jönsson - 2014 - Journal of Philosophical Logic 43 (4):725-759.
    Holistic theories of meaning have, at least since Dummett’s Frege: The Philosophy of language, been assumed to be problematic from the perspective of the incremental nature of natural language learning. In this essay I argue that the general relationship between holism and language learning is in fact the opposite of that claimed by Dummett. It is only given a particular form of language learning, and a particular form of holism, that there is a problem at (...)
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  48.  6
    Large Language Model Displays Emergent Ability to Interpret Novel Literary Metaphors.Nicholas Ichien, Dušan Stamenković & Keith J. Holyoak - 2024 - Metaphor and Symbol 39 (4):296-309.
    Despite the exceptional performance of large language models (LLMs) on a wide range of tasks involving natural language processing and reasoning, there has been sharp disagreement as to whether their abilities extend to more creative human abilities. A core example is the interpretation of novel metaphors. Here we assessed the ability of GPT-4, a state-of-the-art large language model, to provide natural-language interpretations of a recent AI benchmark (Fig-QA dataset), novel literary metaphors drawn from Serbian (...)
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  49. What Game Theory can do for NLG: the case of vague language.Kees van Deemter - unknown
    This informal position paper brings together some recent developments in formal semantics and pragmatics to argue that the discipline of Game Theory is well placed to become the theoretical backbone of Natural Language Generation. To demonstrate some of the strengths and weaknesses of the Game-Theoretical approach, we focus on the utility of vague expressions. More specifically, we ask what light Game Theory can shed on the question when an NLG system should generate vague language.
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  50. Linguistics and natural logic.George Lakoff - 1970 - Synthese 22 (1-2):151 - 271.
    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 (...)
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