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: 60.0
    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: 60.0
    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: 60.0
    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: 58.0
    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: 52.0
    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: 52.0
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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: 50.0
    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. Jia-Li du & Ping-Fang Yu (2012). A Computational Linguistic Approach to Natural Language Processing with Applications to Garden Path Sentences Analysis. Cognitive Science 35:38.score: 50.0
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  9. 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: 50.0
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  10. 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: 50.0
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  11. Nick Chater & Christopher D. Manning (2006). Linguistics, Computational Linguistics and Cognitive Science. Trends in Cognitive Sciences 10 (7):335-344.score: 50.0
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  12. 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: 50.0
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  13. Paul Gochet & Michel Kefer (forthcoming). Roland HAUSSER: Foundations of Computational Linguistics: Human-Computer Communication in Natural Language. Revue Internationale de Philosophie.score: 50.0
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  14. Xiuming Huang (forthcoming). Old Men and Women in Glasses: A Headache for Computational Linguists. Contrastes.score: 50.0
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  15. Dymetman Marc (1998). Group Theory and Computational Linguistics. Journal of Logic, Language and Information 7 (4).score: 50.0
     
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  16. Janyce M. Wiebe & William J. Rapaport (1988). Proceedings of the 26th Annual Meeting of the Association for Computational Linguistics.score: 50.0
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  17. M. Van Zaanen & C. De la Higuera (2009). Computational Linguistic Aspects of Grammatical Inference, 30-31 March. The Reasoner 3 (5):10-10.score: 50.0
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  18. Jacolien Rij, Hedderik Rijn & Petra Hendriks (2013). How WM Load Influences Linguistic Processing in Adults: A Computational Model of Pronoun Interpretation in Discourse. Topics in Cognitive Science 5 (3):564-580.score: 42.0
    This paper presents a study of the effect of working memory load on the interpretation of pronouns in different discourse contexts: stories with and without a topic shift. We discuss a computational model (in ACT-R, Anderson, 2007) to explain how referring expressions are acquired and used. On the basis of simulations of this model, it is predicted that WM constraints only affect adults' pronoun resolution in stories with a topic shift, but not in stories without a topic shift. This (...)
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  19. 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: 42.0
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  20. Harald Maurer (2009). Paul Smolensky, Géraldine Legendre: The Harmonic Mind. From Neural Computation to Optimality-Theoretic Grammar. Vol. 1: Cognitive Architecture. Vol. 2: Linguistic and Philosophical Implications. [REVIEW] Journal for General Philosophy of Science 40 (1):141-147.score: 40.0
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  21. William Ramsey (2009). The Harmonic Mind: From Neural Computation to Optimality-Theoretic Grammar-Volume 1: Cognitive Architecture and Volume 2: Linguistic and Philosophical Implications. [REVIEW] Philosophical Books 50 (3):172-184.score: 40.0
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  22. Amichai Kronfeld (1990). Reference and Computation: An Essay in Applied Philosophy of Language. Cambridge University Press.score: 40.0
    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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  23. Frederic B. Fitch, J. B. Rosser, A. R. Turquette, R. M. Martin, Nelson Goodman, Soren Hallden & Paul Bernays (2013). The Journal of Symbolic Logic Publishes Original Scholarly Work in Symbolic Logic. Founded in 1936, It has Become the Leading Research Journal in the Field. The Journal Aims to Represent Logic Broadly, Including its Connections with Mathematics and Philosophy as Well as Newer Aspects Related to Computer Science and Linguistics. [REVIEW] Journal of Symbolic Logic 106 (107).score: 40.0
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  24. Patrick Saint-Dizier & Evelyne Viegas (eds.) (1995). Computational Lexical Semantics. Cambridge University Press.score: 40.0
    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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  25. Timothy Smiley, Frederic B. Fitch, Shih-Chao Liu, S. C. Kleene, Keith Lehrer, Thomas E. Patton, Maria Kokoszynska, Arto Salomaa, Abraham Robinson & Gerald E. Sacks (2013). The Journal of Symbolic Logic Publishes Original Scholarly Work in Symbolic Logic. Founded in 1936, It has Become the Leading Research Journal in the Field. The Journal Aims to Represent Logic Broadly, Including its Connections with Mathematics and Philosophy as Well as Newer Aspects Related to Computer Science and Linguistics. [REVIEW] Journal of Symbolic Logic 175 (176).score: 40.0
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  26. Victor Harnik, Terrence S. Millar, Michael L. Wage, Saharon Shelah, Helmut Schwichtenberg, Daniel Lascar, Bruno Poizat, Warren D. Goldfarb, On Carnap & Hugues Leblanc (2013). The Journal of Symbolic Logic Publishes Original Scholarly Work in Symbolic Logic. Founded in 1936, It has Become the Leading Research Journal in the Field. The Journal Aims to Represent Logic Broadly, Including its Connections with Mathematics and Philosophy as Well as Newer Aspects Related to Computer Science and Linguistics. [REVIEW] Journal of Symbolic Logic 309 (318).score: 40.0
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  27. Laszlo Kalmar, Janos Suranyi, W. V. Quine, Ernest Nagel, George Dw Berry, George W. Brown, Th Skolem, Evert W. Beth, Max Black & H. E. Vaughan (2013). The Journal of Symbolic Logic Publishes Original Scholarly Work in Symbolic Logic. Founded in 1936, It has Become the Leading Research Journal in the Field. The Journal Aims to Represent Logic Broadly, Including its Connections with Mathematics and Philosophy as Well as Newer Aspects Related to Computer Science and Linguistics. [REVIEW] Journal of Symbolic Logic 102 (104).score: 40.0
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  28. Leopold Lowenheim, S. C. Kleene, Paul Bernays, Saunders MacLane, Ernest Nagel, Albert Wohlstetter, J. C. C. McKinsey, Charles A. Baylis, Carl G. Hempel & C. H. Langford (2013). The Journal of Symbolic Logic Publishes Original Scholarly Work in Symbolic Logic. Founded in 1936, It has Become the Leading Research Journal in the Field. The Journal Aims to Represent Logic Broadly, Including its Connections with Mathematics and Philosophy as Well as Newer Aspects Related to Computer Science and Linguistics. [REVIEW] Journal of Symbolic Logic 43 (44).score: 40.0
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  29. John Willats & Fredo Durand (2005). Defining Pictorial Style: Lessons From Linguistics and Computer Graphics. [REVIEW] Axiomathes 15 (3):319-351.score: 40.0
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  30. Bernd Jaspert (1971). R. A. Wisbey : A Complete Word Index to the Speculum Ecclesiae (Early Middle High German and Latin). With a Reverse Index to the Graphic Forms (Compendia. Computer-Generated Aids to Literary and Linguistic Research, Vol. 2), W. S. Maney and Son Ltd, Leeds (England) 1968, IX, 319 Pp. [REVIEW] Zeitschrift für Religions- Und Geistesgeschichte 23 (1):158-158.score: 40.0
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  31. Andrzej Mostowski, W. V. Quine, Arthur Francis Smullyan, Virgil Hinshaw, Alonzo Church, Charles A. Baylis, Maurice L'Abbe, Max Black, Paul Bernays & David Nelson (2013). The Journal of Symbolic Logic Publishes Original Scholarly Work in Symbolic Logic. Founded in 1936, It has Become the Leading Research Journal in the Field. The Journal Aims to Represent Logic Broadly, Including its Connections with Mathematics and Philosophy as Well as Newer Aspects Related to Computer Science and Linguistics. [REVIEW] Journal of Symbolic Logic 63 (64).score: 40.0
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  32. 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: 40.0
    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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  33. 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.score: 40.0
  34. 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: 40.0
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  35. D. Terence Langendoen (1979). Linguistics Must Be Computational Too. Behavioral and Brain Sciences 2 (3):470-471.score: 40.0
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  36. John Nerbonne (1996). Computational Semantics—Linguistics and Processing. In Shalom Lappin (ed.), The Handbook of Contemporary Semantic Theory. Blackwell Reference. 459--82.score: 40.0
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  37. Frank Nuessel (forthcoming). The Computational Model of Linguistic Theory. Semiotics:128-139.score: 40.0
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  38. Edward P. Stabler (1984). Berwick and Weinberg on Linguistics and Computational Psychology. Cognition 17 (2):155-179.score: 40.0
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  39. Jacob L. Mey & Hiroshi Tamura (1992). Barriers to Communication in a Computer Age. AI and Society 6 (1):62-77.score: 34.0
    When people speak about “communication barriers”, what they usually think about are such things as the limitations set by human nature itself, or the constraints that are inherent in the tools we use for communicating. As an example of the first, consider the limited range of the naked human voice; for the second, we may think of the limitations imposed by such primitive communicative devices as the bonfire, the heliograph, or an old-fashioned megaphone.Our contribution draws attention to the fact that, (...)
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  40. Arthur C. Graesser & Danielle S. McNamara (2011). Computational Analyses of Multilevel Discourse Comprehension. Topics in Cognitive Science 3 (2):371-398.score: 32.0
    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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  41. Floriana Grasso (2002). Towards Computational Rhetoric. Informal Logic 22 (3).score: 32.0
    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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  42. Pierrette Bouillon & Federica Busa (eds.) (2001). The Language of Word Meaning. Cambridge University Press.score: 30.0
    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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  43. Philip J. Nickel (2013). Artificial Speech and Its Authors. Minds and Machines 23 (4):489-502.score: 30.0
    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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  44. Martha Stone Palmer (2006). Semantic Processing for Finite Domains. Cambridge University Press.score: 30.0
    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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  45. Erik Andersson (ed.) (1978). Working Papers on Computer Processing of Syntactic Data. Research Institute, Åbo Akademi Foundation.score: 30.0
     
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  46. Jacques Jayez (1989). Problems of Context and Knowledge. Argumentation 3 (3):303-319.score: 30.0
    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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  47. 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: 30.0
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  48. J. Lambek (2008). Pregroup Grammars and Chomsky's Earliest Examples. Journal of Logic, Language and Information 17 (2):141-160.score: 28.0
    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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  49. W. Oberschelp (1998). The Sorcerer and the Apprentice. Human-Computer Interaction Today. AI and Society 12 (1-2):97-104.score: 28.0
    Human-computer interaction today has got a touch of magic: Without understanding the causal coherence, using a computer seems to become the art to use the right spell with the mouse as the magic wand — the sorcerer's staff. Goethes's poem admits an allegoric interpretation. We explicate the analogy between using a computer and casting a spell with emphasis on teaching magic skills. The art to create an ergonomic user interface has to take care of various levels of skills for the (...)
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  50. Vladimir Pericliev (1999). The Prospects for Machine Discovery in Linguistics. Foundations of Science 4 (4):463-482.score: 28.0
    The article reports the results from the developmentof four data-driven discovery systems, operating inlinguistics. The first mimics the induction methods ofJohn Stuart Mill, the second performs componentialanalysis of kinship vocabularies, the third is ageneral multi-class discrimination program, and thefourth finds logical patterns in data. These systemsare briefly described and some arguments are offeredin favour of machine linguistic discovery. Thearguments refer to the strength of machines incomputationally complex tasks, the guaranteedconsistency of machine results, the portability ofmachine methods to new tasks and (...)
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