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  1. Evandro Agazzi (1981). Intentionality and Artificial Intelligence. Epistemologia 4:195.
  2. Varol Akman (1998). Situations and Artificial Intelligence. Minds and Machines 8 (4):475-477.
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  3. Aho Alfred & Jeffrey Ullman (1972). The Theory of Parsing, Translation and Compiling. Prentice-Hall.
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  4. Istvan S. Berkeley (2008). What the <0.70, 1.17, 0.99, 1.07> is a Symbol? Minds and Machines 18 (1):93-105.
    The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...)
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  5. Patrick Blackburn (2005). Representation and Inference for Natural Language: A First Course in Computational Semantics. Center for the Study of Language and Information.
    How can computers distinguish the coherent from the unintelligible, recognize new information in a sentence, or draw inferences from a natural language passage? Computational semantics is an exciting new field that seeks answers to these questions, and this volume is the first textbook wholly devoted to this growing subdiscipline. The book explains the underlying theoretical issues and fundamental techniques for computing semantic representations for fragments of natural language. This volume will be an essential text for computer scientists, linguists, and anyone (...)
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  6. Patrick Blackburn & Johan Bos (2003). Computational Semantics. Theoria 18 (1):27-45.
    In this article we discuss what constitutes a good choice of semantic representation, compare different approaches of constructing semantic representations for fragments of natural language, and give an overview of recent methods for employing inference engines for natural language understanding tasks.
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  7. Patrick Blackburn & Michael Kohlhase (2004). Inference and Computational Semantics. Journal of Logic, Language and Information 13 (2):117-120.
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  8. Damjan Bojadžiev (1989). Davidson's Semantics and Computational Understanding of Language. Grazer Philosophische Studien 36:133-139.
    Evaluating the usefulness of Davidson's semantics to computational understanding of language requires an examination of the role of a theory of truth in characterizing sentence meaning and logical form, and in particular of the connection between meaning and belief. The suggested conclusion is that the relevance of Davidson's semantics for computational semantics lies not so much in its methods and particular proposals of logical form as in its general orientation towards "desubstantializing" meaning.
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  9. L. Böök (1999). Representationalism and the Metonymic Fallacy. Synthese 118 (1):13-30.
    Representationalism in cognitive science holds that semantic meaning should be explained by representations in the mind or brain. In this paper it is argued that semantic meaning should instead be explained by an abstract theory of semantic machines -- machines with predicative capability. The concept of a semantic machine (like that of a Turing machine or of Dennett's intentional systems ) is not a physical concept -- although it has physical implementations. The predicative competence of semantic machines is defined in (...)
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  10. J. Bos & S. Pulman (eds.) (2011). Proceedings of the International Conference on Computational Semantics 9.
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  11. Johan Bos (2004). Computational Semantics in Discourse: Underspecification, Resolution, and Inference. Journal of Logic, Language and Information 13 (2):139-157.
    In this paper I introduce a formalism for natural language understandingbased on a computational implementation of Discourse RepresentationTheory. The formalism covers a wide variety of semantic phenomena(including scope and lexical ambiguities, anaphora and presupposition),is computationally attractive, and has a genuine inference component. Itcombines a well-established linguistic formalism (DRT) with advancedtechniques to deal with ambiguity (underspecification), and isinnovative in the use of first-order theorem proving techniques.The architecture of the formalism for natural language understandingthat I advocate consists of three levels of processing:underspecification, (...)
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  12. Eugene Charniak & Yorick Wilks (eds.) (1976). Computational Semantics: An Introduction to Artificial Intelligence and Natural Language Comprehension. Distributors for the U.S.A. And Canada, Elsevier/North Holland.
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  13. Xiang Chen (2001). Perceptual Symbols and Taxonomy Comparison. Philosophy of Science 3 (September):S200-S212.
    Many recent cognitive studies reveal that human cognition is inherently perceptual, sharing systems with perception at both the conceptual and the neural levels. This paper introduces Barsalou's theory of perceptual symbols and explores its implications for philosophy of science. If perceptual symbols lie in the heart of conceptual processing, the process of attribute selection during concept representation, which is critical for defining similarity and thus for comparing taxonomies, can no longer be determined solely by background beliefs. The analogous nature of (...)
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  14. Andy Clark & Pete Mandik (2002). Selective Representing and World-Making. Minds and Machines 12 (3):383-395.
    In this paper, we discuss the thesis of selective representing — the idea that the contents of the mental representations had by organisms are highly constrained by the biological niches within which the organisms evolved. While such a thesis has been defended by several authors elsewhere, our primary concern here is to take up the issue of the compatibility of selective representing and realism. In this paper we hope to show three things. First, that the notion of selective representing (...)
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  15. Tanya De Villiers, Why Peirce Matters : The Symbol in Deacon’s Symbolic Species.
    The original publication is available at htt://www.sciencedirect.com.
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  16. Shimon Edelman (1995). Representation, Similarity, and the Chorus of Prototypes. Minds and Machines 5 (1):45-68.
    It is proposed to conceive of representation as an emergent phenomenon that is supervenient on patterns of activity of coarsely tuned and highly redundant feature detectors. The computational underpinnings of the outlined concept of representation are (1) the properties of collections of overlapping graded receptive fields, as in the biological perceptual systems that exhibit hyperacuity-level performance, and (2) the sufficiency of a set of proximal distances between stimulus representations for the recovery of the corresponding distal contrasts between stimuli, as in (...)
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  17. M. Teresa Espinal (1992). On the Representation of Linguistic Information. In Jes Ezquerro (ed.), Cognition, Semantics and Philosophy. Kluwer. 75--105.
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  18. Tim Fernando (2001). Ambiguous Discourse in a Compositional Context. An Operational Perspective. Journal of Logic, Language and Information 10 (1):63-86.
    The processing of sequences of (English) sentences is analyzedcompositionally through transitions that merge sentences, rather thandecomposing them. Transitions that are in a precise senseinertial are related to disjunctive and non-deterministic approaches toambiguity. Modal interpretations are investigated, inducing variousequivalences on sequences.
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  19. Jerry A. Fodor (1979). In Reply to Philip Johnson-Laird's What's Wrong with Grandma's Guide to Procedural Semantics: A Reply to Jerry Fodor. Cognition 7 (March):93-95.
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  20. Jerry A. Fodor (1978). Tom Swift and His Procedural Grandmother. Cognition 6 (September):229-47.
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  21. Stan Franklin (1997). Action Patterns, Conceptualization, and Artificial Intelligence. Behavioral and Brain Sciences 20 (1):23-24.
    This commentary connects some of Glenberg's ideas to similar ideas from artificial intelligence. Second, it briefly discusses hidden assumptions relating to meaning, representations, and projectable properties. Finally, questions about mechanisms, mental imagery, and conceptualization in animals are posed.
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  22. Michael J. Giordano (1981). Icon and Symbol. Semiotics:29-37.
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  23. Raffaela Giovagnoli (2013). Representation, Analytic Pragmatism and AI. In Gordana Dodig-Crnkovic Raffaela Giovagnoli (ed.), Computing Nature. 161--169.
    Our contribution aims at individuating a valid philosophical strategy for a fruitful confrontation between human and artificial representation. The ground for this theoretical option resides in the necessity to find a solution that overcomes, on the one side, strong AI (i.e. Haugeland) and, on the other side, the view that rules out AI as explanation of human capacities (i.e. Dreyfus). We try to argue for Analytic Pragmatism (AP) as a valid strategy to present arguments for a form of weak AI (...)
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  24. Ben Goertzel, Moshe Looks, Ari Heljakka & Cassio Pennachin (2007). Toward a Pragmatic Understanding of the Cognitive Underpinnings of Symbol Grounding. In R. Gudwin & J. Queiroz (eds.), Semiotics and Intelligent Systems Development. Idea Group Inc..
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  25. Stefan Gruner (2013). Eric Winsberg: Science in the Age of Computer Simulation. [REVIEW] Minds and Machines 23 (2):251-254.
  26. Robert F. Hadley (1990). Truth Conditions and Procedural Semantics. In Philip P. Hanson (ed.), Information, Language and Cognition. University of British Columbia Press.
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  27. Philip P. Hanson (ed.) (1990). Information, Language and Cognition. University of British Columbia Press.
  28. Philip N. Johnson-Laird (1978). What's Wrong with Grandma's Guide to Procedural Semantics: A Reply to Jerry Fodor. Cognition 9 (September):249-61.
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  29. Philip N. Johnson-Laird (1977). Procedural Semantics. Cognition 5 (3):189-214.
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  30. A. Joshi, Bruce H. Weber & Ivan A. Sag (eds.) (1981). Elements of Discourse Understanding. Cambridge University Press.
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  31. Brendan Kitts (1999). Representation Operators and Computation. Minds and Machines 9 (2):223-240.
    This paper analyses the impact of representation and search operators on Computational Complexity. A model of computation is introduced based on a directed graph, and representation and search are defined to be the vertices and edges of this graph respectively. Changing either the representation or the search algorithm leads to different possible complexity classes. The final section explores the role of representation in reducing time complexity in Artificial Intelligence.
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  32. W. Lehnert (ed.) (1982). Strategies for Natural Language Processing. Lawrence Erlbaum.
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  33. Michael Levison (2012). The Semantic Representation of Natural Language. Bloomsbury Academic.
    Introduction -- Basic concepts -- Previous approaches -- Semantic expressions: introduction -- Formal issues -- Semantic expressions: basic features -- Advanced features -- Applications: capture -- Three little pigs -- Applications: creation.
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  34. Antonio Lieto (2014). A Computational Framework for Concept Representation in Cognitive Systems and Architectures: Concepts as Heterogeneous Proxytypes. Proceedings of 5th International Conference on Biologically Inspired Cognitive Architectures, Boston, MIT, Pocedia Computer Science, Elsevier:1-9.
    In this paper a possible general framework for the representation of concepts in cognitive artificial systems and cognitive architectures is proposed. The framework is inspired by the so called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The contribution of the paper is twofold: on one hand, it aims at providing a novel theoretical hypothesis for the debate about concepts in cognitive sciences by providing (...)
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  35. Christopher Manning & Hinrich Schütze (1999). Foundations of Statistical Natural Language Processing. MIT Press.
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  36. Drew McDermott (1978). Tarskian Semantics, or No Notation Without Denotation. Cognitive Science 2 (3):277-82.
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  37. Christophe Menant, Introduction to a Systemic Theory of Meaning (Jan 2010 Update).
    Information and Meaning are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning: - Semiotics - Phenomenology - Analytic Philosophy - Psychology No general coverage is available for the notion of meaning. We propose to complement this lack by a systemic approach to meaning generation.
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  38. Christophe Menant (2014). Introduction to a Systemic Theory of Meaning (July 2014 Update). Dissertation,
    Information and Meaning are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning: - Semiotics - Phenomenology - Analytic Philosophy - Psychology No general coverage is available for the notion of meaning. We propose to complement this lack by a systemic approach to meaning generation.
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  39. Christophe Menant (2011). Computation on Information, Meaning and Representations. An Evolutionary Approach. World Scientific.
    Understanding computation as “a process of the dynamic change of information” brings to look at the different types of computation and information. Computation of information does not exist alone by itself but is to be considered as part of a system that uses it for some given purpose. Information can be meaningless like a thunderstorm noise, it can be meaningful like an alert signal, or like the representation of a desired food. A thunderstorm noise participates to the generation of meaningful (...)
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  40. Marcin Mostowski (1998). Computational Semantics for Monadic Quantifiers. Journal of Applied Non--Classical Logics 8 (1-2):107--121.
    The paper gives a survey of known results related to computational devices (finite and push–down automata) recognizing monadic generalized quantifiers in finite models. Some of these results are simple reinterpretations of descriptive—feasible correspondence theorems from finite–model theory. Additionally a new result characterizing monadic quantifiers recognized by push down automata is proven.
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  41. Christopher Parisien & Paul Thagard (2008). Robosemantics: How Stanley the Volkswagen Represents the World. [REVIEW] Minds and Machines 18 (2):169-178.
    One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car’s successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that (...)
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  42. Jeff Pelletier, Book Reviews. [REVIEW]
    Computational semantics is the study of how to represent meaning in a way that computers can use. For the authors of this textbook, this study includes the representation of the meaning of natural language in logic formalisms, the recognition of certain relations that hold within this formalization (such as synonymy, consistency, and implication), and the computational implementation of all this. I think that, while there probably are not many courses devoted to computational semantics, this book could profitably be incorporated into (...)
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  43. Donald R. Perlis (1994). Putting One's Foot in One's Head -- Part 2: How. In Eric Dietrich (ed.), Thinking Computers and Virtual Persons. Academic Press. 435-455.
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  44. Donald R. Perlis (1991). Putting One's Foot in One's Head -- Part 1: Why. Noûs 25 (September):435-55.
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  45. Giuseppe Primiero & Bjorn Jespersen (2010). Two Kinds of Procedural Semantics for Privative Modification. Lecture Notes in Artificial Intelligence 6284:251--271.
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  46. William J. Rapaport (1995). Understanding Understanding: Syntactic Semantics and Computational Cognition. Philosophical Perspectives 9:49-88.
    John Searle once said: "The Chinese room shows what we knew all along: syntax by itself is not sufficient for semantics. (Does anyone actually deny this point, I mean straight out? Is anyone actually willing to say, straight out, that they think that syntax, in the sense of formal symbols, is really the same as semantic content, in the sense of meanings, thought contents, understanding, etc.?)." I say: "Yes". Stuart C. Shapiro has said: "Does that make any sense? Yes: Everything (...)
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  47. William J. Rapaport (1988). Syntactic Semantics: Foundations of Computational Natural Language Understanding. In James H. Fetzer (ed.), Aspects of AI. Kluwer.
    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 (...)
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  48. Erich Rast (2014). Review of Fenstad's "Grammar, Geometry & Brain". [REVIEW] Studia Logica 102 (1):219-223.
    In this small book logician and mathematician Jens Erik Fenstad addresses some of the most important foundational questions of linguistics: What should a theory of meaning look like and how might we provide the missing link between meaning theory and our knowledge of how the brain works? The author’s answer is twofold. On the one hand, he suggests that logical semantics in the Montague tradition and other broadly conceived symbolic approaches do not suffice. On the other hand, he does not (...)
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  49. Robert Rovetto (2011). The Shape of Shapes: An Ontological Exploration. In Janna Hastings, Oliver Kutz, Mehul Bhatt & Stefano Borgo (eds.), CEUR Workshop Proceedings Vol-812. Editors.
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  50. Robert J. Rovetto, An Ontological Architecture for Orbital Debris Data.
    The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris and broader SSA domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain (...)
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