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  1. Varol Akman (1998). Situations and Artificial Intelligence. Minds and Machines 8 (4):475-477.
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  2. 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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  3. 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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  4. 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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  5. Patrick Blackburn & Michael Kohlhase (2004). Inference and Computational Semantics. Journal of Logic, Language and Information 13 (2):117-120.
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  6. 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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  7. 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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  8. J. Bos & S. Pulman (eds.) (2011). Proceedings of the International Conference on Computational Semantics 9.
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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. Jerry A. Fodor (1978). Tom Swift and His Procedural Grandmother. Cognition 6 (September):229-47.
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  15. 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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  16. 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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  17. Philip P. Hanson (ed.) (1990). Information, Language and Cognition. University of British Columbia Press.
  18. 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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  19. Philip N. Johnson-Laird (1977). Procedural Semantics. Cognition 5 (3):189-214.
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  20. A. Joshi, Bruce H. Weber & Ivan A. Sag (eds.) (1981). Elements of Discourse Understanding. Cambridge University Press.
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  21. 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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  22. W. Lehnert (ed.) (1982). Strategies for Natural Language Processing. Lawrence Erlbaum.
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  23. 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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  24. Drew McDermott (1978). Tarskian Semantics, or No Notation Without Denotation. Cognitive Science 2 (3):277-82.
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  25. 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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  26. 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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  27. 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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  28. 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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  29. Donald R. Perlis (1991). Putting One's Foot in One's Head -- Part 1: Why. Noûs 25 (September):435-55.
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  30. 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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  31. 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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  32. Erich Rast (2013). Review of Fenstad's "Grammar, Geometry & Brain&Quot;. [REVIEW] Studia Logica 101 (5).
    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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  33. B. Smith (1987). The Correspondence Continuum. Csli 87.
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  34. Mark Steedman & Matthew Stone, Is Semantics Computational?
    Both formal semantics and cognitive semantics are the source of important insights about language. By developing precise statements of the rules of meaning in fragmentary, abstract languages, formalists have been able to offer perspicuous accounts of how we might come to know such rules and use them to communicate with others. Conversely, by charting the overall landscape of interpretations, cognitivists have documented how closely interpretations draw on the commonsense knowledge that lets us make our way in the world. There is (...)
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  35. Jakub Szymanik & Marcin Zajenkowski (2009). Understanding Quantifiers in Language. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
    We compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and pushdown automata is psychologically relevant. Our research improves upon hypothesis and explanatory power of recent neuroimaging studies as well as provides evidence for the claim that human linguistic abilities are constrained by computational complexity.
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  36. Jakub Szymanik & Marcin Zajenkowski (2009). Comprehension of Simple Quantifiers. Empirical Evaluation of a Computational Model. Cognitive Science: A Multidisciplinary Journal 34 (3):521-532.
    We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality.
    In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves upon hypothesis and (...)
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  37. Jan van Eijck, A Program for Computational Semantics.
    Just as war can be viewed as continuation of diplomacy using other means, computational semantics is continuation of logical analysis of natural language by other means. For a long time, the tool of choice for this used to be Prolog. In our recent textbook we argue (and try to demonstrate by example) that lazy functional programming is a more appropriate tool. In the talk we will lay out a program for computational semantics, by linking computational semantics to the general analysis (...)
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  38. Jan van Eijck, Computational Semantics, Type Theory, and Functional Programming.
    An emerging standard for polymorphically typed, lazy, purely functional programming is Haskell, a language named after Haskell Curry. Haskell is based on (polymorphically typed) lambda calculus, which makes it an excellent tool for computational semantics.
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  39. Jan van Eijck, Computational Semantics with Functional Programming.
    Almost forty years ago Richard Montague proposed to analyse natural language with the same tools as formal languages. In particular, he gave formal semantic analyses of several interesting fragments of English in terms of typed logic. This led to the development of Montague grammar as a particular style of formal analysis of natural language.
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  40. Y. Wilks (1990). Form and Content in Semantics. Synthese 82 (3):329-51.
    This paper continues a strain of intellectual complaint against the presumptions of certain kinds of formal semantics (the qualification is important) and their bad effects on those areas of artificial intelligence concerned with machine understanding of human language. After some discussion of the use of the term epistemology in artificial intelligence, the paper takes as a case study the various positions held by McDermott on these issues and concludes, reluctantly, that, although he has reversed himself on the issue, there was (...)
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  41. Y. Wilks (1982). Some Thoughts on Procedural Semantics. In W. Lehnert (ed.), Strategies for Natural Language Processing. Lawrence Erlbaum.
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  42. Terry Winograd (1985). Moving the Semantic Fulcrum. Linguistics and Philosophy 8 (February):91-104.
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  43. W. Woods (1986). Problems in Procedural Semantics. In Zenon W. Pylyshyn & W. Demopolous (eds.), Meaning and Cognitive Structure. Ablex.
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  44. W. Woods (1981). Procedural Semantics as a Theory of Meaning. In A. Joshi, Bruce H. Weber & Ivan A. Sag (eds.), Elements of Discourse Understanding. Cambridge University Press.
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