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1
Parallel processing in speech perception with local and global representations of linguistic context
In: eLife (2022)
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Using surprisal and fMRI to map the neural bases of broad and local contextual prediction during natural language comprehension ...
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3
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
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4
Connecting Documents, Words, and Languages Using Topic Models
Yang, Weiwei. - 2019
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5
Assessing Composition in Sentence Vector Representations ...
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6
Relating lexical and syntactic processes in language: Bridging research in humans and machines
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7
Guided Probabilistic Topic Models for Agenda-setting and Framing
Nguyen, Viet An. - 2015
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8
Soft syntactic constraints for Arabic-English hierarchical phrase-based translation
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 26 (2012) 1-2, 137-157
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9
Crowdsourced Monolingual Translation
Hu, Chang. - 2012
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10
Decision Tree-based Syntactic Language Modeling
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11
Modeling Dependencies in Natural Languages with Latent Variables
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12
Exploiting syntactic relationships in a phrase-based decoder: an exploration
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 24 (2010) 2, 123-140
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OLC Linguistik
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13
Gibbs Sampling for the Uninitiated
In: DTIC (2010)
Abstract: This document is intended for computer scientists who would like to try out a Markov Chain Monte Carlo (MCMC) technique, particularly to do inference with Bayesian models on problems related to text processing. We try to keep theory to the absolute minimum needed, though we work through the details much more explicitly than you usually see even in "introductory" explanations. That means we've attempted to be ridiculously explicit in our exposition and notation. After providing the reasons and reasoning behind Gibbs sampling (and at least nodding our heads in the direction of theory), we work through an example application in detail -- the derivation of a Gibbs sampler for a Naive Bayes model. Along with the example, we discuss some practical implementation issues, including the integrating out of continuous parameters when possible. We conclude with some pointers to literature that we've found to be somewhat more friendly to uninitiated readers. ; Sponsored in part by the GALE program of the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), and the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), through the Army Research Laboratory. LAMP-TR-153.
Keyword: *BAYES THEOREM; *GIBBS SAMPLING; *MARKOV CHAIN MONTE CARLO; *MARKOV PROCESSES; *MONTE CARLO METHOD; *NAIVE BAYES MODEL; *NATURAL LANGUAGE; *SAMPLING; *STATISTICAL INFERENCE; *TEXT PROCESSING; ALGORITHMS; AUTOCORRELATION; BETA DISTRIBUTION; COMPUTATIONAL LINGUISTICS; CONVERGENCE; DIRICHLET DISTRIBUTION; DISTRIBUTION; DOCUMENT LABELS; DOCUMENTS; EXPECTED VALUES; INITIALIZATION; INTEGRALS; JOINT DISTRIBUTION; LABELS; Linguistics; MAP(MAXIMUM A POSTERIORI ESTIMATION); MAXIMUM LIKELIHOOD ESTIMATION; MCMC(MARKOV CHAIN MONTE CARLO); Numerical Mathematics; PARAMETER ESTIMATION; PARAMETERS; POSTERIOR PROBABILITIES; PROBABILITY ESTIMATION; STATE SPACE; Statistics and Probability; VALUE
URL: http://www.dtic.mil/docs/citations/ADA523027
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA523027
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14
Structured local exponential models for machine translation
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15
A Formal Model of Ambiguity and its Applications in Machine Translation
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16
Extending Phrase-Based Decoding with a Dependency-Based Reordering Model
In: DTIC (2009)
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17
Extending Phrase-Based Decoding with a Dependency-Based Reordering Model
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18
COMPUTATIONAL ANALYSIS OF THE CONVERSATIONAL DYNAMICS OF THE UNITED STATES SUPREME COURT
Hawes, Timothy. - 2009
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19
Fine-Grained Linguistic Soft Constraints on Statistical Natural Language Processing Models
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20
Generalizing Word Lattice Translation
In: DTIC (2008)
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