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  1. Chris Barker & Chung-Chieh Shan (2014). Continuations and Natural Language. OUP Oxford.
    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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  2. Steven Bird (1995). Computational Phonology a Constraint-Based Approach. Monograph Collection (Matt - Pseudo).
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  3. Patrick Blackburn, Claire Gardent & Maarten De Rijke (1996). Rich Ontologies for Tense and Aspect. In Jerry Seligman & Dag Westerståhl (eds.), Logic, Language and Computation. Csli Publications, Stanford
  4. Peter Bosch & Rob van der Sandt (eds.) (1995). Focus and Natural Language Processing. Ibm Deutschland.
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  5. Michael R. Brent (1996). Advances in the Computational Study of Language Acquisition. Cognition 61 (1-2):1-38.
  6. Angelo Cangelosi & Domenico Parisi (2002). Computer Simulation: A New Scientific Approach to the Study of Language Evolution. In A. Cangelosi & D. Parisi (eds.), Simulating the Evolution of Language. Springer-Verlag 3--28.
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  7. Eugene Charniak (1982). Context Recognition in Language Comprehension. In W. Lehnert (ed.), Strategies for Natural Language Processing. Lawrence Erlbaum 435--454.
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  8. Nick Chater & Christopher D. Manning (2006). Linguistics, Computational Linguistics and Cognitive Science. Trends in Cognitive Sciences 10 (7):335-344.
  9. Stergios Chatzikyriakidis & Zhaohui Luo (2014). Natural Language Inference in Coq. 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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  10. Berlin Chen, Hsin-min Wang & Lin-Shan Lee (2001). An HMM/N-Gram-Based Linguistic Processing Approach for Mandarin Spoken Document Retrieval. Corpus 1 (2):1-1.
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  11. Morten H. Christiansen & Padraic Monaghan (2016). Division of Labor in Vocabulary Structure: Insights From Corpus Analyses. 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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  12. Robin Cohen (1990). A Processing Model for the Analysis of One-Way Arguments in Discourse. 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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  13. Ann Copestake (1995). The Representation of Group Denoting Nouns in a Lexical Knowledge Base. In Patrick Saint-Dizier & Evelyne Viegas (eds.), Computational Lexical Semantics. Cambridge University Press 207--231.
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  14. Matthew W. Crocker (1990). Multiple Meta-Interpreters in a Logical Model of Sentence Processing. Department of Artificial Intelligence, University of Edinburgh.
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  15. Keśavacandra Dāśa (1991). Relations in Knowledge Representation: An Interdisciplinary Study in Nyāya, Mīmāṁsā, Vyākaraṇa, Tantra, Modern Linguistics, and Artificial Intelligence in Computer Application. Sri Satguru Publications.
  16. 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
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  17. Manuel De Vega (2008). Levels of Embodied Meaning: From Pointing to Counterfactuals. In Manuel de Vega, Arthur M. Glenberg & Arthur C. Graesser (eds.), Symbols and Embodiment: Debates on Meaning and Cognition. Oxford University Press
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  18. William Dolan, Lucy Vanderwende, Stephen Richardson & Bill Dolan (2000). Polysemy in a Broad-Coverage Natural Language Processing System. In Yael Ravin & Claudia Leacock (eds.), Polysemy: Theoretical and Computational Approaches. Oxford University Press
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  19. Marc Dymetman (1998). Group Theory and Computational Linguistics. 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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  20. Shimon Edelman, Rich Syntax From a Raw Corpus: Unsupervised Does It.
    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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  21. Shimon Edelman, Unsupervised Efficient Learning and Representation of Language Structure.
    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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  22. Bruce Edmonds, The Pragmatic Roots of Context.
    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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  23. Tim Fernando, Entailments in Finite-State Temporality.
    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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  24. 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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  25. Katherine Forbes, Eleni Miltsakaki, Rashmi Prasad, Anoop Sarkar, Aravind Joshi & Bonnie Webber (2003). D-LTAG System: Discourse Parsing with a Lexicalized Tree-Adjoining Grammar. [REVIEW] 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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  26. Joyce Friedman & David S. Warren (1978). A Parsing Method for Montague Grammars. 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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  27. Joseph S. Fulda (2006). A Plea for Automated Language-to-Logical-Form Converters. 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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  28. Bernhard Ganter & Guy W. Mineau (2000). Conceptual Structures Logical, Linguistic, and Computational Issues : 8th International Conference on Conceptual Structures, Iccs 2000, Darmstadt, Germany, August 2000 : Proceedings. [REVIEW]
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  29. Paul L. Garvin (1966). Natural Language and the Computer. Foundations of Language 2 (1):105-106.
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  30. Cliff Goddard (2000). Polysemy: A Problem of Definition. In Yael Ravin & Claudia Leacock (eds.), Polysemy: Theoretical and Computational Approaches. Oxford University Press 129--151.
  31. Arthur C. Graesser, Moongee Jeon, Zhiqiang Cai, Danielle S. McNamara, J. Auracher & W. van Peer (2008). Automatic Analyses of Language, Discourse, and Situation Models. In Jan Auracher & Willie van Peer (eds.), New Beginnings in Literary Studies. Cambridge Scholars Pub.
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  32. Daniel Grodner & Julie Sedivy (2011). The Effect of Speaker-Specific Information on Pragmatic Inferences. In Edward Gibson & Neal J. Pearlmutter (eds.), The Processing and Acquisition of Reference. The 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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  33. Thomas Hills, Mounir Maouene, Josita Maouene, Adam Sheya & Linda B. Smith (2008). Categorical Structure in Early Semantic Networks of Nouns. 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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  34. Daniel Jurafsky & James H. Martin (2000). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall.
    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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  35. Edward L. Keenan (1983). Facing the Truth: Some Advantages of Direct Interpretation. [REVIEW] Linguistics and Philosophy 6 (3):335 - 371.
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  36. Ruth M. Kempson (1996). Semantics, Pragmatics, and Natural-Language Interpretation. In Shalom Lappin (ed.), The Handbook of Contemporary Semantic Theory. Blackwell 561--598.
  37. F. Kiefer (1966). Computational Linguistics III. Foundations of Language 2 (4):405-408.
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  38. Dan Klein & Christopher D. Manning, Distributional Phrase Structure Induction.
    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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  39. Dan Klein & Christopher D. Manning, Natural Language Grammar Induction Using a Constituent-Context Model.
    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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  40. 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.
    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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  41. Oren Kolodny, Arnon Lotem & Shimon Edelman (2014). Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition. 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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  42. Claudia Kunze (1990). A Syllable-Based Net-Linguistic Approach to Lexical Access. In G. Dorffner (ed.), Konnektionismus in Artificial Intelligence Und Kognitionsforschung. Berlin: Springer-Verlag 28--37.
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  43. Christopher Manning, Natural Language Grammar Induction Using a Constituent-Context Model.
    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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  44. G. Miller & Claudia Leacock (2000). Lexical Representations for Sentence Processing. In Yael Ravin & Claudia Leacock (eds.), Polysemy: Theoretical and Computational Approaches. Oxford University Press 152--160.
  45. Christof Monz (1999). Automatic Ambiguity Resolution in Natural Language, Alexander Franz. Journal of Logic, Language and Information 8 (1):111-114.
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  46. Roger K. Moore (2009). Spoken Language Processing by Machine. In Gareth Gaskell (ed.), Oxford Handbook of Psycholinguistics. OUP Oxford
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  47. Michael Moortgat (2001). Logical Aspects of Computational Linguistics Third International Conference, Lalc '98, Grenoble, France, December 14-16, 1998 : Selected Papers'. [REVIEW]
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  48. Stefania Nuccorini (ed.) (2002). Phrases and Phraseology – Data and Descriptions. Peter Lang Verlag.
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  49. Bilge Say & Varol Akman (1997). Current Approaches to Punctuation in Computational Linguistics. Philosophical Explorations.
    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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  50. 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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1 — 50 / 53