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Rule Based Transliteration Scheme for English to Punjabi [<Journal>]
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| 3 |
Improving the quality of Gujarati-Hindi Machine Translation through part-of-speech tagging and stemmer-assisted transliteration [<Journal>]
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| 4 |
Part of Speech Tagging of Marathi Text Using Trigram Method [<Journal>]
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| 6 |
The Latent Relation Mapping Engine: Algorithm and Experiments [<Journal>]
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| 8 |
Experiments on predictability of word in context and information rate in natural language [<Journal>]
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Manin, Dmitrii. - : Keldysh Institute of Applied Mathematics (KIAM) RAS
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| 9 |
LLC : the journal of digital scholarship in the humanities ; journal of the Association for Literary and Linguistic Computing and The Association for Computers and the Humanities [<Journal>]
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Oxford : Oxford Univ. Press
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BLLDB
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OLC Linguistik
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UB Frankfurt Linguistik
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| 10 |
LLC : the journal of digital scholarship in the humanities ; journal of the Association for Literary and Linguistic Computing and The Association for Computers and the Humanities [<Journal>]
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Oxford : Oxford Univ. Press
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IDS Mannheim
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| 11 |
Corpus-based Learning of Analogies and Semantic Relations [<Journal>]
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| 12 |
Measuring praise and criticism: Inference of semantic orientation from association [<Journal>]
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| 15 |
The adaptive advantage of symbolic theft over sensorimotor toil: Grounding language in perceptual categories [<Journal>]
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Abstract:
Using neural nets to simulate learning and the genetic algorithm to simulate evolution in a toy world of mushrooms and mushroom-foragers, we place two ways of acquiring categories into direct competition with one another: In (1) "sensorimotor toil,” new categories are acquired through real-time, feedback-corrected, trial and error experience in sorting them. In (2) "symbolic theft,” new categories are acquired by hearsay from propositions – boolean combinations of symbols describing them. In competition, symbolic theft always beats sensorimotor toil. We hypothesize that this is the basis of the adaptive advantage of language. Entry-level categories must still be learned by toil, however, to avoid an infinite regress (the “symbol grounding problem”). Changes in the internal representations of categories must take place during the course of learning by toil. These changes can be analyzed in terms of the compression of within-category similarities and the expansion of between-category differences. These allow regions of similarity space to be separated, bounded and named, and then the names can be combined and recombined to describe new categories, grounded recursively in the old ones. Such compression/expansion effects, called "categorical perception" (CP), have previously been reported with categories acquired by sensorimotor toil; we show that they can also arise from symbolic theft alone. The picture of natural language and its origins that emerges from this analysis is that of a powerful hybrid symbolic/sensorimotor capacity, infinitely superior to its purely sensorimotor precursors, but still grounded in and dependent on them. It can spare us from untold time and effort learning things the hard way, through direct experience, but it remain anchored in and translatable into the language of experience.
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Keyword:
Artificial Intelligence; Cognitive Psychology; Computational Linguistics; Evolution; Evolutionary Psychology; Neural Nets
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URL: http://cogprints.org/2036/
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| 16 |
Bootstrapping grounded symbols by minimal autonomous robots [<Journal>]
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| 17 |
Book Review--Ronald Cole (editor-in-chief), Joseph Mariani, Hans Uszkoreit, Annie Zaenen, and Victor Zue, eds., Survey of the State of the Art in Human Language Technology [<Journal>]
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| 19 |
An Analysis of English Punctuation: The Special Case of Comma [<Journal>]
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