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Hits 1 – 20 of 66
1
A Refutation of Finite-State Language Models through Zipf’s Law for Factual Knowledge
Łukasz Dębowski
In: Entropy ; Volume 23 ; Issue 9 (2021)
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2
STATISTICAL RELATIONAL LEARNING AND SCRIPT INDUCTION FOR TEXTUAL INFERENCE
Mooney,Raymond
. - 2017
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3
Confound and control in language experiments ...
Alday, Phillip M.
;
Sassenhagen, Jona
. - : figshare, 2016
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4
Confound and control in language experiments ...
Alday, Phillip M.
;
Sassenhagen, Jona
. - : figshare, 2016
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5
Structural Complexity in Linguistic Systems Research Topic 3: Mathematical Sciences
Crutchfield, James P
In: DTIC (2015)
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6
A Fast Variational Approach for Learning Markov Random Field Language Models
Jernite, Yacine
;
Rush, Alexander M
;
Sontag, David
In: DTIC (2015)
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7
Learning to Understand Natural Language with Less Human Effort
Krishnamurthy, Jayant
In: DTIC (2015)
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8
The role of markup in the digital humanities
Schmidt, Desmond
In: Historical Social Research ; 37 ; 3 ; 125-146 ; Kontroversen um die Digitalen Geisteswissenschaften / Controversies around the digital humanities (2015)
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9
Mandarin listeners can learn non-native lexical tones through distributional learning
Ong, Jia
(S31400);
Burnham, Denis K.
(R7357);
Escudero, Paola
(R16636). - : U.K., University of Glasgow, 2015
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10
A nonparametric Bayesian perspective for machine learning in partially-observed settings ...
Akova, Ferit
. - : IUPUI University Library, 2014
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11
A nonparametric Bayesian perspective for machine learning in partially-observed settings
Akova, Ferit
. - 2014
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12
The Negations of Conjunctions, Conditionals, and Disjunctions
Khemlani, Sangeet
;
Orenes, Isabel
;
Johnson-Laird, P N
In: DTIC (2014)
Abstract:
How do reasoners understand and formulate denials of compound assertions, such as conjunctions and disjunctions? A theory based on mental models postulates that individuals enumerate models of the various possibilities consistent with the assertions. It therefore predicts a novel interaction: in affirmations, conjunctions,A and B, which refer to one possibility, should be easier to understand than disjunctions, A or B, which refer to more than one possibility; in denials conjunctions, not (A and B), which refer to more than one possibility, should be harder to understand than disjunctions not (A or B), which do not. Conditionals are ambiguous and they should be of intermediate difficulty. Experiment 1 corroborated this trend with a task in which the participants selected which possibilities were consistent with assertions, such as: Bob denied that he wore a yellow shirt and he wore blue pants on Tuesday. Experiment 2 likewise showed that participants' own formulations of verbal denials yielded the same trend in which denials of conjunctions were harder than denials of conditionals,which in turn were harder than denials of disjunctions. ; Published in Acta Psychologica, v151 p1-7, 2014.
Keyword:
*EXPERIMENTAL PSYCHOLOGY
;
*LOGIC
;
*NATURAL LANGUAGE
;
*PERFORMANCE(HUMAN)
;
*PSYCHOLINGUISTICS
;
COGNITIVE PROCESSES
;
CONDITIONALS
;
CONJUNCTIONS
;
DECISION MAKING
;
DENIALS
;
DISJUNCTIONS
;
INFORMATION PROCESSING
;
Linguistics
;
MATHEMATICAL PREDICTION
;
MEMORY(PSYCHOLOGY)
;
MENTAL ABILITY
;
MENTAL MODELS
;
NEGATION
;
ONLINE SYSTEMS
;
PATTERN RECOGNITION
;
Psychology
;
REASONING
;
SEMANTICS
;
STATISTICAL INFERENCE
;
Statistics and Probability
URL:
http://www.dtic.mil/docs/citations/ADA619029
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA619029
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13
What's Wrong With Automatic Speech Recognition (ASR) and How Can We Fix It?
Morgan, Nelson
;
Wegmann, Steven
;
Cohen, Jordan
In: DTIC (2013)
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14
Identität in Erzählung und im Erzählen: Versuch einer Bestimmung der Besonderheit des narrativen Diskurses für die sprachliche Verfassung von ldentität
Bamberg, Michael
In: Journal für Psychologie ; 7 ; 1 ; 43-55 (2012)
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15
Coherent Demodulation of Nonstationary Random Processes
Clark, Charles Pascal
. - 2012
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16
Incremental Syntactic Language Models for Phrase-Based Translation
Schwartz, Lane
;
Callison-Burch, Chris
;
Schuler, William
...
In: DTIC (2011)
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17
Using Linguistic Knowledge in Statistical Machine Translation
Zbib, Rabih M.
In: DTIC (2010)
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18
Related Entity Finding: University of Waterloo at TREC 2010 Entity Track
Vechtomova, Olga
In: DTIC (2010)
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19
Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques
Ge, Ruifang
In: DTIC (2010)
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20
Gibbs Sampling for the Uninitiated
Resnik, Philip
;
Hardisty, Eric
In: DTIC (2010)
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