Search results for 'Computational linguistics' (try it on Scholar)

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  1. Bilge Say & Varol Akman (1997). Current Approaches to Punctuation in Computational Linguistics. Philosophical Explorations.score: 180.0
    Some recent studies in computational linguistics have aimed to take advantage of various cues presented by punctuation marks. This short survey is intended to summarise these research efforts and additionally, to outline a current perspective for the usage and functions of punctuation marks. We conclude by presenting an information-based framework for punctuation, influenced by treatments of several related phenomena in computational linguistics.
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  2. Serge Thill, Sebastian Padó & Tom Ziemke (2014). On the Importance of a Rich Embodiment in the Grounding of Concepts: Perspectives From Embodied Cognitive Science and Computational Linguistics. Topics in Cognitive Science 6 (3):545-558.score: 180.0
    The recent trend in cognitive robotics experiments on language learning, symbol grounding, and related issues necessarily entails a reduction of sensorimotor aspects from those provided by a human body to those that can be realized in machines, limiting robotic models of symbol grounding in this respect. Here, we argue that there is a need for modeling work in this domain to explicitly take into account the richer human embodiment even for concrete concepts that prima facie relate merely to simple actions, (...)
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  3. G. Neumann (2008). A Computational Linguistics Perspective on the Anticipatory Drive. Constructivist Foundations 4 (1):26-28.score: 180.0
    Open peer commentary on the target article “How and Why the Brain Lays the Foundations for a Conscious Self” by Martin V. Butz. Excerpt: In this commentary to Martin V. Butz’s target article I am especially concerned with his remarks about language (§33, §§71–79, §91) and modularity (§32, §41, §48, §81, §§94–98). In that context, I would like to bring into discussion my own work on computational models of self-monitoring (cf. Neumann 1998, 2004). In this work I explore the (...)
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  4. Marc Dymetman (1998). Group Theory and Computational Linguistics. Journal of Logic, Language and Information 7 (4):461-497.score: 158.0
    There is currently much interest in bringing together the tradition of categorial grammar, and especially the Lambek calculus, with the recent paradigm of linear logic to which it has strong ties. One active research area is designing non-commutative versions of linear logic (Abrusci, 1995; Retoré, 1993) which can be sensitive to word order while retaining the hypothetical reasoning capabilities of standard (commutative) linear logic (Dalrymple et al., 1995). Some connections between the Lambek calculus and computations in groups have long been (...)
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  5. Alexander Koller, Ralph Debusmann, Malte Gabsdil & Kristina Striegnitz (2004). Put My Galakmid Coin Into the Dispenser and Kick It: Computational Linguistics and Theorem Proving in a Computer Game. [REVIEW] Journal of Logic, Language and Information 13 (2):187-206.score: 156.0
    We combine state-of-the-art techniques from computational linguisticsand theorem proving to build an engine for playing text adventures,computer games with which the player interacts purely through naturallanguage. The system employs a parser for dependency grammar and ageneration system based on TAG, and has components for resolving andgenerating referring expressions. Most of these modules make heavy useof inferences offered by a modern theorem prover for descriptionlogic. Our game engine solves some problems inherent in classical textadventures, and is an interesting test case (...)
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  6. Igor A. Bolshakov & Sofia N. Galicia-Haro (2006). Computational Linguistics Research-Corpus-Based Knowledge Acquisition-Web-Based Measurements of Intra-Collocational Cohesion in Oxford Collocations Dictionary. In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer-Verlag. 3878--93.score: 152.0
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  7. Daniel Jurafsky & James H. Martin (2000). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall.score: 150.0
    The first of its kind to thoroughly cover language technology at all levels and with all modern technologies this book takes an empirical approach to the ...
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  8. Gerhard Jager & Structural Rules Residuation (2004). Alexander Koller, Ralph Debusmann, Malte Gabsdil, and Kristina Striegnitz/Put My Galakmid Coin Into the Dispenser and Kick It: Computational Linguistics and Theorem Proving in a Computer Game 187–206. Journal of Logic, Language and Information 13:537-539.score: 150.0
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  9. Simonetta Bassi, Felice Dell'orletta, Daniele Esposito & Alessandro Lenci (2006). Computational Linguistics Meets Philosophy: A Latent Semantic Analy-Sis of Giordano Bruno's Texts. Rinascimento 46:631-647.score: 150.0
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  10. Nick Chater & Christopher D. Manning (2006). Linguistics, Computational Linguistics and Cognitive Science. Trends in Cognitive Sciences 10 (7):335-344.score: 150.0
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  11. Philippe de Groote, Glyn Morrill & Christian Retoré (2001). Logical Aspects of Computational Linguistics (LACL'01). In P. Bouquet (ed.), Lecture Notes in Artificial Intelligence. Kluwer.score: 150.0
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  12. Paul Gochet & Michel Kefer (forthcoming). Roland HAUSSER: Foundations of Computational Linguistics: Human-Computer Communication in Natural Language. Revue Internationale de Philosophie.score: 150.0
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  13. Dymetman Marc (1998). Group Theory and Computational Linguistics. Journal of Logic, Language and Information 7 (4).score: 150.0
     
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  14. Janyce M. Wiebe & William J. Rapaport (1988). Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics.score: 150.0
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  15. Patrick Saint-Dizier & Evelyne Viegas (eds.) (1995). Computational Lexical Semantics. Cambridge University Press.score: 120.0
    Lexical semantics has become a major research area within computational linguistics, drawing from psycholinguistics, knowledge representation, computer algorithms and architecture. Research programmes whose goal is the definition of large lexicons are asking what the appropriate representation structure is for different facets of lexical information. Among these facets, semantic information is probably the most complex and the least explored.Computational Lexical Semantics is one of the first volumes to provide models for the creation of various kinds of computerised lexicons (...)
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  16. Janyce M. Wiebe & William J. Rapaport (1988). A Computational Theory of Perspective and Reference in Narrative. In Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics.score: 120.0
    Narrative passages told from a character's perspective convey the character's thoughts and perceptions. We present a discourse process that recognizes characters' thoughts and perceptions in third-person narrative. An effect of perspective on reference in narrative is addressed: References in passages told from the perspective of a character reflect the character's beliefs. An algorithm that uses the results of our discourse process to understand references with respect to an appropriate set of beliefs is presented.
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  17. Micah B. Goldwater, Noah D. Goodman, Stephen Wechsler & Gregory L. Murphy (2009). Relational and Role-Governed Categories: Views From Psychology, Computational Modeling, and Linguistics. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.score: 120.0
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  18. D. Terence Langendoen (1979). Linguistics Must Be Computational Too. Behavioral and Brain Sciences 2 (3):470-471.score: 120.0
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  19. John Nerbonne (1996). Computational Semantics—Linguistics and Processing. In Shalom Lappin (ed.), The Handbook of Contemporary Semantic Theory. Blackwell Reference. 459--82.score: 120.0
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  20. Edward P. Stabler (1984). Berwick and Weinberg on Linguistics and Computational Psychology. Cognition 17 (2):155-179.score: 120.0
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  21. Amichai Kronfeld (1990). Reference and Computation: An Essay in Applied Philosophy of Language. Cambridge University Press.score: 100.0
    This book deals with a major problem in the study of language: the problem of reference. The ease with which we refer to things in conversation is deceptive. Upon closer scrutiny, it turns out that we hardly ever tell each other explicitly what object we mean, although we expect our interlocutor to discern it. Amichai Kronfeld provides an answer to two questions associated with this: how do we successfully refer, and how can a computer be programmed to achieve this? Beginning (...)
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  22. Floriana Grasso (2002). Towards Computational Rhetoric. Informal Logic 22 (3).score: 96.0
    The notions of argument and argumentation have become increasingly ubiquitous in Artificial Intelligence research, with various application and interpretations. Less attention has been, however, specifically devoted to rhetorical argument The work presented in this paper aims at bridging this gap, by proposing a framework for characterising rhetorical argumentation, based on Perelman and Olbrechts-Tyteca's New Rhetoric. The paper provides an overview of the state of the art of computational work based on, or dealing with, rhetorical aspects of argumentation, before presenting (...)
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  23. Arthur C. Graesser & Danielle S. McNamara (2011). Computational Analyses of Multilevel Discourse Comprehension. Topics in Cognitive Science 3 (2):371-398.score: 92.0
    The proposed multilevel framework of discourse comprehension includes the surface code, the textbase, the situation model, the genre and rhetorical structure, and the pragmatic communication level. We describe these five levels when comprehension succeeds and also when there are communication misalignments and comprehension breakdowns. A computer tool has been developed, called Coh-Metrix, that scales discourse (oral or print) on dozens of measures associated with the first four discourse levels. The measurement of these levels with an automated tool helps researchers track (...)
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  24. Pierrette Bouillon & Federica Busa (eds.) (2001). The Language of Word Meaning. Cambridge University Press.score: 90.0
    This volume is a collection of original contributions from outstanding scholars in linguistics, philosophy and computational linguistics exploring the relation between word meaning and human linguistic creativity. The papers present different aspects surrounding the question of what is word meaning, a problem that has been the center of heated debate in all those disciplines that directly or indirectly are concerned with the study of language and of human cognition. The discussions are centered around the newly emerging view (...)
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  25. Philip J. Nickel (2013). Artificial Speech and Its Authors. Minds and Machines 23 (4):489-502.score: 90.0
    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 intelligence (...)
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  26. Martha Stone Palmer (2006). Semantic Processing for Finite Domains. Cambridge University Press.score: 90.0
    A primary problem in the area of natural language processing has been semantic analysis. This book looks at the semantics of natural languages in context. It presents an approach to the computational processing of English text that combines current theories of knowledge representation and reasoning in Artificial Intelligence with the latest linguistic views of lexical semantics. The book will interest postgraduates and researchers in computational linguistics as well as industrial research groups specializing in natural language processing.
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  27. Shira Calamaro & Gaja Jarosz (2014). Learning General Phonological Rules From Distributional Information: A Computational Model. Cognitive Science 38 (8):n/a-n/a.score: 90.0
    Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony (Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as (...)
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  28. Jacques Jayez (1989). Problems of Context and Knowledge. Argumentation 3 (3):303-319.score: 90.0
    In spite of alleged differences in purpose, descriptive and computational linguistics share many problems, due to the fact that any precise study on language needs some form of knowledge representation. This constraint is mostly apparent when interpretation of sentences takes into account elements of the so-called “context”. The parametrization of context, i.e. the explicit listing of features relevant to some intepretation task, is difficult because it requires flexible formal structures for understanding or simulating inferential behaviour, as well as (...)
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  29. Jakub Szymanik (2010). Computational Complexity of Polyadic Lifts of Generalized Quantifiers in Natural Language. Linguistics and Philosophy 33 (3):215-250.score: 72.0
    We study the computational complexity of polyadic quantifiers in natural language. This type of quantification is widely used in formal semantics to model the meaning of multi-quantifier sentences. First, we show that the standard constructions that turn simple determiners into complex quantifiers, namely Boolean operations, iteration, cumulation, and resumption, are tractable. Then, we provide an insight into branching operation yielding intractable natural language multi-quantifier expressions. Next, we focus on a linguistic case study. We use computational complexity results to (...)
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  30. J. Lambek (2008). Pregroup Grammars and Chomsky's Earliest Examples. Journal of Logic, Language and Information 17 (2):141-160.score: 72.0
    Pregroups are partially ordered monoids in which each element has two “adjoints”. Pregroup grammars provide a computational approach to natural languages by assigning to each word in the mental dictionary a type, namely an element of the pregroup freely generated by a partially ordered set of basic types. In this expository article, the attempt is made to introduce linguists to a pregroup grammar of English by looking at Chomsky’s earliest examples.
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  31. Patrick Blackburn & Edith Spaan (1993). A Modal Perspective on the Computational Complexity of Attribute Value Grammar. Journal of Logic, Language and Information 2 (2):129-169.score: 72.0
    Many of the formalisms used in Attribute Value grammar are notational variants of languages of propositional modal logic, and testing whether two Attribute Value Structures unify amounts to testing for modal satisfiability. In this paper we put this observation to work. We study the complexity of the satisfiability problem for nine modal languages which mirror different aspects of AVS description formalisms, including the ability to express re-entrancy, the ability to express generalisations, and the ability to express recursive constraints. Two main (...)
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  32. Erik Andersson (ed.) (1978). Working Papers on Computer Processing of Syntactic Data. Research Institute, Åbo Akademi Foundation.score: 70.0
     
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  33. Kevin P. Jones (ed.) (1987). Meaning, the Frontier of Informatics: Informatics 9: Proceedings of a Conference Jointly Sponsored by Aslib, the Aslib Informatics Group and the Information Retrieval Specialist Group of the British Computer Society, King's College, Cambridge, 26-27 March 1987. [REVIEW] Aslib.score: 70.0
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  34. Jakub Szymanik (2009). Quantifiers in TIME and SPACE. Computational Complexity of Generalized Quantifiers in Natural Language. Dissertation, University of Amsterdamscore: 66.0
    In the dissertation we study the complexity of generalized quantifiers in natural language. Our perspective is interdisciplinary: we combine philosophical insights with theoretical computer science, experimental cognitive science and linguistic theories. -/- In Chapter 1 we argue for identifying a part of meaning, the so-called referential meaning (model-checking), with algorithms. Moreover, we discuss the influence of computational complexity theory on cognitive tasks. We give some arguments to treat as cognitively tractable only those problems which can be computed in polynomial (...)
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  35. Geoffrey K. Pullum & Kyle Rawlins (2007). Argument or No Argument? Linguistics and Philosophy 30 (2):277 - 287.score: 66.0
    We examine an argument for the non-context-freeness of English that has received virtually no discussion in the literature. It is based on adjuncts of the form ‘X or no X’, where X is a nominal. The construction has been held to exemplify unbounded syntactic reduplication. We argue that although the argument can be made in a mathematically valid form, its empirical basis is not claimed unbounded syntactic identity between nominals does not always hold in attested cases, and second, an understanding (...)
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  36. Marten van Schijndel, Andy Exley & William Schuler (2013). A Model of Language Processing as Hierarchic Sequential Prediction. Topics in Cognitive Science 5 (3):522-540.score: 66.0
    Computational models of memory are often expressed as hierarchic sequence models, but the hierarchies in these models are typically fairly shallow, reflecting the tendency for memories of superordinate sequence states to become increasingly conflated. This article describes a broad-coverage probabilistic sentence processing model that uses a variant of a left-corner parsing strategy to flatten sentence processing operations in parsing into a similarly shallow hierarchy of learned sequences. The main result of this article is that a broad-coverage model with constraints (...)
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  37. Robert D. Levine & W. Detmar Meurers (2006). Head-Driven Phrase Structure Grammar: Linguistic Approach, Formal Foundations, and Computational Realization. In Keith Brown (ed.), Encyclopedia of Language and Linguistics. Elsevier.score: 66.0
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  38. Shuly Wintner & Nissim Francez (1999). Off-Line Parsability and the Well-Foundedness of Subsumption. Journal of Logic, Language and Information 8 (1):1-16.score: 62.0
    Typed feature structures are used extensively for the specification of linguistic information in many formalisms. The subsumption relation orders TFSs by their information content. We prove that subsumption of acyclic TFSs is well founded, whereas in the presence of cycles general TFS subsumption is not well founded. We show an application of this result for parsing, where the well-foundedness of subsumption is used to guarantee termination for grammars that are off-line parsable. We define a new version of off-line parsability that (...)
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  39. Yael Ravin & Claudia Leacock (eds.) (2000). Polysemy: Theoretical and Computational Approaches. Oxford University Press.score: 60.0
    Polysemy is a term used in semantic and lexical analysis to describe a word with multiple meanings. Although such words present few difficulties in everyday communication, they do pose near-intractable problems for linguists and lexicographers. The contributors in this volume consider the implications of these problems for linguistic theory and how they may be addressed in computational linguistics.
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  40. Shimon Edelman, Bridging Computational, Formal and Psycholinguistic Approaches to Language.score: 60.0
    We compare our model of unsupervised learning of linguistic structures, ADIOS [1, 2, 3], to some recent work in computational linguistics and in grammar theory. Our approach resembles the Construction Grammar in its general philosophy (e.g., in its reliance on structural generalizations rather than on syntax projected by the lexicon, as in the current generative theories), and the Tree Adjoining Grammar in its computational characteristics (e.g., in its apparent affinity with Mildly Context Sensitive Languages). The representations learned (...)
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  41. Dylan Glynn & Kerstin Fischer (eds.) (2010). Quantitative Methods in Cognitive Semantics: Corpus-Driven Approaches. De Gruyter Mouton.score: 60.0
    Corpus-driven Cognitive Semantics Introduction to the field Dylan Glynn Is quantitative empirical research possible for the study of semantics?1 More ...
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  42. Bob Carpenter (1997). Type-Logical Semantics. Mit Press.score: 60.0
     
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  43. Hans Kamp, Alessandro Lenci & James Pustejovsky, Computational Models of Language Meaning in Context (Dagstuhl Seminar 13462).score: 60.0
    This report documents the program and the outcomes of Dagstuhl Seminar 13462 "Computational Models of Language Meaning in Context". The seminar addresses one of the most significant issues to arise in contemporary formal and computational models of language and inference: that of the role and expressiveness of distributional models of semantics and statistically derived models of language and linguistic behavior. The availability of very large corpora has brought about a near revolution in computational linguistics and language (...)
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  44. Michael Levison (2012). The Semantic Representation of Natural Language. Bloomsbury Academic.score: 60.0
    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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  45. Max M. Louwerse & Rolf A. Zwaan (2009). Language Encodes Geographical Information. Cognitive Science 33 (1):51-73.score: 60.0
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  46. Geoff Barnbrook (2013). Collocation: Applications and Implications. Palgrave Macmillan.score: 60.0
     
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  47. P. Braffort & F. van Scheepen (eds.) (1968). Automation in Language Translation and Theorem Proving. Brussels, Commission of the European Communities, Directorate-General for Dissemination of Information.score: 60.0
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  48. M. A. K. Halliday (1999/2006). Construing Experience Through Meaning: A Language-Based Approach to Cognition. Continuum.score: 60.0
  49. Byeong-Ho Kang & Debbie Richards (eds.) (2010). Knowledge Management and Acquisition for Smart Systems and Services: 11th International Workshop, Pkaw 2010, Daegu, Korea, August 20 - September 3, 2010: Proceedings. [REVIEW] Springer.score: 60.0
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