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|a 9781684000791
|q paperback
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|a P98
|b .P745 2022
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| 082 |
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|a 410.285
|2 23/eng/20220720
|
| 245 |
0 |
0 |
|a Probabilistic approaches to linguistic theory /
|c edited by Jean-Philippe Bernardy, Rasmus Blanck, Stergios Chatzikyriakidis, Shalom Lappin, Aleksandre Maskharashvili.
|
| 264 |
|
1 |
|a Stanford, California :
|b Center for the Study of Language and Information,
|c [2022]
|
| 300 |
|
|
|a x, 453 pages :
|b illustrations ;
|c 23 cm
|
| 336 |
|
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|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a unmediated
|b n
|2 rdamedia
|
| 338 |
|
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|a volume
|b nc
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references.
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| 505 |
0 |
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|a Introduction -- 1. Computational Morphology / Christo Kirov and Richard Sproat -- 2. Something Old, Something New : Grammar-based CCG Parsing with Transformer Models / Stephen Clark -- 3. Probabilistic Lexical Semantics: From Gaussian Embeddings to Bernoulli Fields / Guy Emerson -- 4. The origins of vagueness / Peter Sutton -- 5. Bayesian Inference Semantics for Natural Language / Jean-Philippe Bernardy, Rasmus Blanck, Stergios Chatzikyriakidis, Shalom Lappin and Aleksandre Maskharashvili -- 6. Probabilistic pragmatics : a dialogical perspective / Bill Noble, Vladislav Maraev, and Ellen Breitholtz -- 7. Neuro-computation for language processing / Vidya Somashekarappa -- 8. Learning Language Games Probabilistically : From Crying to Compositionality / Robin Cooper, Jonathan Ginzburg and Staffan Larsson -- 9. Distributional Semantics for Situated Spatial Language? Functional, Geometric and Perceptual Perspectives / John D. Kelleher and Simon Dobnik -- 10. Action coordination and learning in dialogue / Arash Eshghi, Christine Howes, and Eleni Gregoromichelaki -- 11. Reanalysis, probability, and the faculty of language / Asad B. Sayeed -- Contributors -- Glossary.
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| 520 |
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|a "During the last two decades, computational linguists, in concert with other researchers in AI, have turned to machine learning and statistical techniques to capture features of natural language and aspects of the learning process that are not easily accommodated in classical algebraic frameworks. These developments are producing a revolution in linguistics in which traditional symbolic systems are giving way to probabilistic and deep learning approaches. This collection features articles that provide background to these approaches, and their application in syntax, semantics, pragmatics, morphology, psycholinguistics, neurolinguistics, and dialogue modeling. Each chapter provides a self-contained introduction to the topic that it covers, making this volume accessible to graduate students and researchers in linguistics, NLP, AI, and cognitive science"--
|c Provided by publisher.
|
| 650 |
|
0 |
|a Computational linguistics.
|
| 650 |
|
0 |
|a Natural language processing (Computer science)
|
| 650 |
|
0 |
|a Deep learning (Machine learning)
|
| 650 |
|
7 |
|a Computational linguistics.
|2 fast
|0 (OCoLC)fst00871998
|
| 650 |
|
7 |
|a Deep learning (Machine learning)
|2 fast
|0 (OCoLC)fst02032663
|
| 650 |
|
7 |
|a Natural language processing (Computer science)
|2 fast
|0 (OCoLC)fst01034365
|
| 700 |
1 |
|
|a Bernardy, Jean-Philippe,
|e editor.
|
| 700 |
1 |
|
|a Blanck, Rasmus,
|e editor.
|
| 700 |
1 |
|
|a Chatzikyriakidis, Stergios,
|e editor.
|
| 700 |
1 |
|
|a Lappin, Shalom,
|e editor.
|
| 700 |
1 |
|
|a Maskharashvili, Aleksandre,
|e editor.
|
| 776 |
0 |
8 |
|i Online version :
|t Probabilistic approaches to linguistic theory
|d Stanford : Center for the Study of Language and Information, 2022
|z 9781684000807
|w (DLC) 2022033334
|
| 929 |
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| 928 |
|
|
|t Library of Congress classification
|a P98.P745 2022
|l JRL
|c JRL-Gen
|i 13150532
|
| 927 |
|
|
|t Library of Congress classification
|a P98.P745 2022
|l JRL
|c JRL-Gen
|e MARO
|b 118325203
|i 10478801
|
