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  1. added 2019-07-29
    Logical Aspects of Computational Linguistics. Celebrating 20 Years of LACL (1996–2016).Maxime Amblard, Philippe de Groote, Sylvain Pogodalla & Christian Rétoré (eds.) - 2016 - Berlin, Germany: Springer.
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  2. added 2019-03-12
    Natural Language Understanding: Methodological Conceptualization.Vitalii Shymko - 2019 - Psycholinguistics 25 (1):431-443.
    This article contains the results of a theoretical analysis of the phenomenon of natural language understanding (NLU), as a methodological problem. The combination of structural-ontological and informational-psychological approaches provided an opportunity to describe the subject matter field of NLU, as a composite function of the mind, which systemically combines the verbal and discursive structural layers. In particular, the idea of NLU is presented, on the one hand, as the relation between the discourse of a specific speech message and the meta-discourse (...)
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  3. added 2018-07-02
    LTAG-Spinal and the Treebank.Lucas Champollion & Aravind K. Joshi - unknown
    We introduce LTAG-spinal, a novel variant of traditional Lexicalized Tree Adjoining Grammar (LTAG) with desirable linguistic, computational and statistical properties. Unlike in traditional LTAG, subcategorization frames and the argument-adjunct distinction are left underspecified in LTAG-spinal. LTAG-spinal with adjunction constraints is weakly equivalent to LTAG. The LTAG-spinal formalism is used to extract an LTAG-spinal Treebank from the Penn Treebank with Propbank annotation. Based on Propbank annotation, predicate coordination and LTAG adjunction structures are successfully extracted. The LTAG-spinal Treebank makes explicit semantic relations (...)
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  4. added 2016-12-08
    Division of Labor in Vocabulary Structure: Insights From Corpus Analyses.Morten H. Christiansen & Padraic Monaghan - 2016 - Topics in Cognitive Science 8 (3):610-624.
    Psychologists have used experimental methods to study language for more than a century. However, only with the recent availability of large-scale linguistic databases has a more complete picture begun to emerge of how language is actually used, and what information is available as input to language acquisition. Analyses of such “big data” have resulted in reappraisals of key assumptions about the nature of language. As an example, we focus on corpus-based research that has shed new light on the arbitrariness of (...)
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  5. added 2016-12-08
    Group Theory and Computational Linguistics.Dymetman Marc - 1998 - Journal of Logic, Language and Information 7 (4):461-497.
    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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  6. added 2016-12-08
    A Computational Theory of Perspective and Reference in Narrative.Janyce M. Wiebe & William J. Rapaport - 1988 - In Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. pp. 131-138.
    Narrative passages told from a character's perspective convey the character's thoughts and perceptions. We present a discourse process that recognizes characters'.
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  7. added 2016-08-02
    Natural Language Grammar Induction Using a Constituent-Context Model.Christopher Manning - manuscript
    This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according to generative PCFG models. In contrast, we employ a simpler probabilistic model over trees based directly on constituent identity and linear context, and use an EM-like iterative procedure to induce structure. This method produces much higher quality analyses, giving the best published results on the ATIS dataset.
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  8. added 2016-08-02
    Spoken Language Processing by Machine.Roger K. Moore - 2009 - In Gareth Gaskell (ed.), Oxford Handbook of Psycholinguistics. Oxford University Press.
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  9. added 2016-08-02
    Logical Aspects of Computational Linguistics Third International Conference, Lalc '98, Grenoble, France, December 14-16, 1998 : Selected Papers'. [REVIEW]Michael Moortgat - 2001
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  10. added 2016-08-02
    Lexical Representations for Sentence Processing.G. Miller & Claudia Leacock - 2000 - In Yael Ravin & Claudia Leacock (eds.), Polysemy: Theoretical and Computational Approaches. Oxford University Press. pp. 152--160.
  11. added 2016-08-02
    Automatic Ambiguity Resolution in Natural Language, Alexander Franz.Christof Monz - 1999 - Journal of Logic, Language and Information 8 (1):111-114.
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  12. added 2016-03-07
    Natural Language Grammar Induction Using a Constituent-Context Model.Dan Klein & Christopher D. Manning - unknown
    This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according to generative PCFG models. In contrast, we employ a simpler probabilistic model over trees based directly on constituent identity and linear context, and use an EM-like iterative procedure to induce structure. This method produces much higher quality analyses, giving the best published results on the ATIS dataset.
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  13. added 2016-03-07
    Semantics, Pragmatics, and Natural-Language Interpretation.Ruth M. Kempson - 1996 - In Shalom Lappin (ed.), The Handbook of Contemporary Semantic Theory. Blackwell. pp. 561--598.
  14. added 2016-03-07
    A Syllable-Based Net-Linguistic Approach to Lexical Access.Claudia Kunze - 1990 - In G. Dorffner (ed.), Konnektionismus in Artificial Intelligence Und Kognitionsforschung. Berlin: Springer-Verlag. pp. 28--37.
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  15. added 2016-03-07
    Facing the Truth: Some Advantages of Direct Interpretation. [REVIEW]Edward L. Keenan - 1983 - Linguistics and Philosophy 6 (3):335 - 371.
  16. added 2016-03-07
    Computational Linguistics III.F. Kiefer - 1966 - Foundations of Language 2 (4):405-408.
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  17. added 2016-02-01
    Natural Language and the Computer.Paul L. Garvin - 1966 - Foundations of Language 2 (1):105-106.
  18. added 2016-01-30
    Categorical Structure in Early Semantic Networks of Nouns.Thomas Hills, Mounir Maouene, Josita Maouene, Adam Sheya & Linda B. Smith - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  19. added 2016-01-29
    The Effect of Speaker-Specific Information on Pragmatic Inferences.Daniel Grodner & Julie Sedivy - 2011 - In Edward Gibson & Neal J. Pearlmutter (eds.), The Processing and Acquisition of Reference. MIT Press.
    Utterances can convey more information than they explicitly encode, and speakers exploit communicative conventions in order to say more with less. However, the burden this places on perceivers is not well understood. This chapter examines the effect of speaker-specific information on pragmatic inferences using data from an experiment which investigated the time course of the use of pragmatic information in language comprehension. Previous evidence suggests that comprehenders who encounter a referential form, including a modifier that commonly indicates contrastiveness, assume that (...)
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  20. added 2016-01-29
    Automatic Analyses of Language, Discourse, and Situation Models.Arthur C. Graesser, Moongee Jeon, Zhiqiang Cai, Danielle S. McNamara, J. Auracher & W. van Peer - 2008 - In Jan Auracher & Willie van Peer (eds.), New Beginnings in Literary Studies. Cambridge Scholars Press.
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  21. added 2016-01-29
    D-LTAG System: Discourse Parsing with a Lexicalized Tree-Adjoining Grammar. [REVIEW]Katherine Forbes, Eleni Miltsakaki, Rashmi Prasad, Anoop Sarkar, Aravind Joshi & Bonnie Webber - 2003 - Journal of Logic, Language and Information 12 (3):261-279.
    We present an implementation of a discourse parsing system for alexicalized Tree-Adjoining Grammar for discourse, specifying the integrationof sentence and discourse level processing. Our system is based on theassumption that the compositional aspects of semantics at thediscourse level parallel those at the sentence level. This coupling isachieved by factoring away inferential semantics and anaphoric features ofdiscourse connectives. Computationally, this parallelism is achievedbecause both the sentence and discourse grammar are LTAG-based and the sameparser works at both levels. The approach to an (...)
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  22. added 2016-01-29
    Polysemy: A Problem of Definition.Cliff Goddard - 2000 - In Yael Ravin & Claudia Leacock (eds.), Polysemy: Theoretical and Computational Approaches. Oxford University Press. pp. 129--151.
  23. added 2016-01-29
    A Parsing Method for Montague Grammars.Joyce Friedman & David S. Warren - 1978 - Linguistics and Philosophy 2 (3):347 - 372.
    The main result in this paper is a method for obtaining derivation trees from sentences of certain formal grammars. No parsing algorithm was previously known to exist for these grammars.Applied to Montague's PTQ the method produces all parses that could correspond to different meanings. The technique directly addresses scope and reference and provides a framework for examining these phenomena. The solution for PTQ is implemented in an efficient and useful computer program.
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  24. added 2015-11-19
    Computer Simulation: A New Scientific Approach to the Study of Language Evolution.Angelo Cangelosi & Domenico Parisi - 2002 - In A. Cangelosi & D. Parisi (eds.), Simulating the Evolution of Language. Springer Verlag. pp. 3--28.
  25. added 2015-11-19
    The Pragmatic Roots of Context.Bruce Edmonds - unknown
    When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference ­ a simple model of (...)
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  26. added 2015-11-19
    Advances in the Computational Study of Language Acquisition.Michael R. Brent - 1996 - Cognition 61 (1-2):1-38.
  27. added 2015-11-19
    Relations in Knowledge Representation: An Interdisciplinary Study in Nyāya, Mīmāṁsā, Vyākaraṇa, Tantra, Modern Linguistics, and Artificial Intelligence in Computer Application.Keśavacandra Dāśa - 1991 - Sri Satguru Publications.
  28. added 2015-10-29
    Natural Language Inference in Coq.Stergios Chatzikyriakidis & Zhaohui Luo - 2014 - Journal of Logic, Language and Information 23 (4):441-480.
    In this paper we propose a way to deal with natural language inference by implementing Modern Type Theoretical Semantics in the proof assistant Coq. The paper is a first attempt to deal with NLI and natural language reasoning in general by using the proof assistant technology. Valid NLIs are treated as theorems and as such the adequacy of our account is tested by trying to prove them. We use Luo’s Modern Type Theory with coercive subtyping as the formal language into (...)
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  29. added 2015-10-29
    Context Recognition in Language Comprehension.Eugene Charniak - 1982 - In W. Lehnert (ed.), Strategies for Natural Language Processing. Lawrence Erlbaum. pp. 435--454.
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  30. added 2015-10-19
    Distributional Phrase Structure Induction.Dan Klein & Christopher D. Manning - unknown
    Unsupervised grammar induction systems commonly judge potential constituents on the basis of their effects on the likelihood of the data. Linguistic justifications of constituency, on the other hand, rely on notions such as substitutability and varying external contexts. We describe two systems for distributional grammar induction which operate on such principles, using part-of-speech tags as the contextual features. The advantages and disadvantages of these systems are examined, including precision/recall trade-offs, error analysis, and extensibility.
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  31. added 2015-10-19
    Unsupervised Efficient Learning and Representation of Language Structure.Shimon Edelman - unknown
    We describe a linguistic pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of corpus data. This is achieved by compactly coding recursively structured constituent patterns, and by placing strings that have an identical backbone and similar context structure into the same equivalence class. The resulting representations constitute an efficient encoding of linguistic knowledge and support systematic generalization to unseen sentences.
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  32. added 2015-10-19
    Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition.Oren Kolodny, Arnon Lotem & Shimon Edelman - 2014 - Cognitive Science 38 (4):227-267.
    We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this (...)
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  33. added 2015-10-19
    Conceptual Structures Logical, Linguistic, and Computational Issues : 8th International Conference on Conceptual Structures, Iccs 2000, Darmstadt, Germany, August 2000 : Proceedings. [REVIEW]Bernhard Ganter & Guy W. Mineau - 2000
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  34. added 2015-10-17
    The Representation of Group Denoting Nouns in a Lexical Knowledge Base.Ann Copestake - 1995 - In Patrick Saint-Dizier & Evelyne Viegas (eds.), Computational Lexical Semantics. Cambridge University Press. pp. 207--231.
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  35. added 2015-10-17
    A Processing Model for the Analysis of One-Way Arguments in Discourse.Robin Cohen - 1990 - Argumentation 4 (4):431-446.
    This paper describes a computational model for analyzing arguments in discourse. In particular, the model describes processes necessary for interpreting one uninterrupted argument from a speaker. The resulting output is a representation for the underlying claim and evidence relations between propositions of the argument. For our processing model we present: (i) a characterization of coherent orderings of propositions, used to limit search for interpretation of each new proposition (ii) a working definition of the evidence relation, used to recognize connections between (...)
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  36. added 2015-10-15
    Rich Syntax From a Raw Corpus: Unsupervised Does It.Shimon Edelman - unknown
    We compare our model of unsupervised learning of linguistic structures, ADIOS [1], 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 by our algorithm are truly (...)
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  37. added 2015-10-15
    Polysemy in a Broad-Coverage Natural Language Processing System.William Dolan, Lucy Vanderwende, Stephen Richardson & Bill Dolan - 2000 - In Yael Ravin & Claudia Leacock (eds.), Polysemy: Theoretical and Computational Approaches. Oxford University Press.
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  38. added 2015-10-14
    Logical Aspects of Computational Linguistics (LACL'01).Philippe de Groote, Glyn Morrill & Christian Retoré - 2001 - In P. Bouquet (ed.), Lecture Notes in Artificial Intelligence. Kluwer Academic Publishers.
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  39. added 2015-10-14
    Multiple Meta-Interpreters in a Logical Model of Sentence Processing.Matthew W. Crocker - 1990 - Department of Artificial Intelligence, University of Edinburgh.
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  40. added 2015-10-13
    Continuations and Natural Language.Chris Barker & Chung-Chieh Shan - 2014 - Oxford University Press.
    This book takes concepts developed by researchers in theoretical computer science and adapts and applies them to the study of natural language meaning. Summarizing over a decade of research, Chris Barker and Chung-chieh Shan put forward the Continuation Hypothesis: that the meaning of a natural language expression can depend on its own continuation.
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  41. added 2015-10-13
    Probabilistic Models of Language Processing and Acquisition.Nick Chater & Christopher D. Manning - 2006 - Trends in Cognitive Sciences 10 (7):335–344.
    Probabilistic methods are providing new explanatory approaches to fundamental cognitive science questions of how humans structure, process and acquire language. This review examines probabilistic models defined over traditional symbolic structures. Language comprehension and production involve probabilistic inference in such models; and acquisition involves choosing the best model, given innate constraints and linguistic and other input. Probabilistic models can account for the learning and processing of language, while maintaining the sophistication of symbolic models. A recent burgeoning of theoretical developments and online (...)
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  42. added 2015-10-13
    Rich Ontologies for Tense and Aspect.Patrick Blackburn, Claire Gardent & Maarten De Rijke - 1996 - In Jerry Seligman & Dag Westerståhl (eds.), Logic, Language and Computation. Csli Publications, Stanford.
  43. added 2015-10-13
    Focus and Natural Language Processing.Peter Bosch & Rob van der Sandt (eds.) - 1995 - Ibm Deutschland.
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  44. added 2015-10-13
    Computational Phonology a Constraint-Based Approach.Steven Bird - 1995
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  45. added 2014-03-26
    Ambiguous Discourse in a Compositional Context. An Operational Perspective.Tim Fernando - 2001 - 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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  46. added 2014-03-22
    Put My Galakmid Coin Into the Dispenser and Kick It: Computational Linguistics and Theorem Proving in a Computer Game. [REVIEW]Alexander Koller, Ralph Debusmann, Malte Gabsdil & Kristina Striegnitz - 2004 - Journal of Logic, Language and Information 13 (2):187-206.
    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 for (...)
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  47. added 2013-05-31
    A Plea for Automated Language-to-Logical-Form Converters.Joseph S. Fulda - 2006 - RASK 24:87-102.
    This has been made available gratis by the publisher. -/- This piece gives the raison d'etre for the development of the converters mentioned in the title. Three reasons are given, one linguistic, one philosophical, and one practical. It is suggested that at least /two/ independent converters are needed. -/- This piece ties together the extended paper "Abstracts from Logical Form I/II," and the short piece providing the comprehensive theory alluded to in the abstract of that extended paper in "Pragmatics, Montague, (...)
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  48. added 2012-01-11
    Entailments in Finite-State Temporality.Tim Fernando - manuscript
    The “surge in use of finite-state methods” ([10]) in computational linguistics has largely, if not completely, left semantics untouched. The present paper is directed towards correcting this situation. Techniques explained in [1] are applied to a fragment of temporal semantics through an approach we call finite-state temporality. This proceeds from the intuition of an event as “a series of snapshots” ([15]; see also [12]), equating snapshots with symbols that collectively form our alphabet. A sequence of snapshots then becomes a string (...)
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  49. added 2010-12-18
    Phrases and Phraseology – Data and Descriptions.Stefania Nuccorini (ed.) - 2002 - Peter Lang Verlag.
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  50. added 2010-12-18
    “Identifying Phrasal Connectives in Italian Using Quantitative Methods”.Edoardo Zamuner, Fabio Tamburini & Cristiana de Sanctis - 2002 - In Stefania Nuccorini (ed.), Phrases and Phraseology – Data and Descriptions. Peter Lang Verlag.
    In recent decades, the analysis of phraseology has made use of the exploration of large corpora as a source of quantitative information about language. This paper intends to present the main lines of work in progress based on this empirical approach to linguistic analysis. In particular, we focus our attention on some problems relating to the morpho-syntactic annotation of corpora. The CORIS/CODIS corpus of contemporary written Italian, developed at CILTA – University of Bologna (Rossini Favretti 2000; Rossini Favretti, Tamburini, De (...)
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