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1
Deriving frequency effects from biases in learning
In: Proceedings of the Linguistic Society of America; Vol 6, No 1 (2021): Proceedings of the Linguistic Society of America; 514–525 ; 2473-8689 (2021)
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2
Probabilistic indigenization effects at the lexis-syntax interface
In: English language and linguistics. - Cambridge : Cambridge Univ. Press 24 (2020) 2, 413-440
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3
Progressive or simple? : A corpus-based study of aspect in World Englishes
In: Corpora. - Edinburgh : Univ. Press 15 (2020) 1, 77-106
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4
Unsupervised Formal Grammar Induction with Confidence
In: Proceedings of the Society for Computation in Linguistics (2020)
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5
When lexical statistics and the grammar conflict: learning and repairing weight effects on stress.
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6
Weight gradience and stress in Portuguese
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7
Cognitive indigenization effects in the English dative alternation
In: Cognitive linguistics. - Berlin ; Boston, Mass. : de Gruyter Mouton 28 (2017) 4, 673-710
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8
Constraints in contact: Animacy in English and Afrikaans genitive variation – a cross-linguistic perspective
In: Glossa: a journal of general linguistics; Vol 2, No 1 (2017); 72 ; 2397-1835 (2017)
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9
Probabilistic Grammar: The view from Cognitive Sociolinguistics
In: Glossa: a journal of general linguistics; Vol 2, No 1 (2017); 62 ; 2397-1835 (2017)
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10
Around the world in three alternations : modeling syntactic variation in varieties of English
In: English world-wide. - Amsterdam [u.a.] : Benjamins 37 (2016) 2, 109-137
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11
Foundations and methods from mathematics to neuroscience : essays inspired by Patrick Suppes
Crangle, Colleen (Hrsg.). - Stanford, Calif. : CSLI Publications, 2014
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UB Frankfurt Linguistik
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12
Lexical Structure, Weightedness, And Information In Sentence Processing
Grove, Kyle. - 2014
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13
Intégration des données d'un lexique syntaxique dans un analyseur syntaxique probabiliste
In: Penser le Lexique-Grammaire. Perspectives actuelles ; 30th International Conference on Lexis and Grammar (LGC'11) ; https://hal-upec-upem.archives-ouvertes.fr/hal-00621647 ; Fryni Kakoyianni-Doa. Penser le Lexique-Grammaire. Perspectives actuelles, Honoré Champion, pp.505-516, 2014, Collection Colloques, congrès et conférences. Sciences du Langage, histoire de la langue et des dictionnaires. 30th International Conference on Lexis and Grammar, Nicosia, Cyprus, 2011, 978-2-7453-2512-9 (2014)
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14
MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing
In: DTIC (2013)
Abstract: This report summarizes the major findings from our research on a semantic information representation framework (SIRF) for visual sensing scenarios. First, the concept and architecture of a cognitive linguistic (CL) based SIRF is introduced. Two levels of information abstraction are proposed within this framework. At the syntactic level, a probabilistic contest free grammar (PCFG) method is employed for information compression and summarization. At the semantic level, a Bayesian network approach is used to achieve semantic concept inference and reasoning. To facilitate the functions of this SIRF, several conceptual primitive modeling methods are proposed, which include a dynamic structure preserving map (DSPM) for individual human action recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA method for human object interaction (HOI) recognition, and a pyramid histogram of gradient (pHOG) method for human object recognition based on gait images. In addition to these conceptual primitive models, two quantities sensing modality utility assessment methods are introduced. They are essentially feature selection methods, one is based sparse imputation and one is based on 11 graph. Extensive experiments on publicly available datasets have been conducted to assess the effectiveness of the proposed methods, and highly competitive and promising results have been observed.
Keyword: *RECOGNITION; *VIDEO IMAGES; BAYESIAN NETWORK; COGNITION; COGNITIVE LINGUISTICS; CONTEXT FREE GRAMMARS; Cybernetics; DETECTORS; GAIT RECOGNITION; GROUP ACTION RECOGNITION; HUMAN OBJECT INTERACTION; LEARNING MACHINES; LINGUISTICS; MACHINE LEARNING; OPTICAL DETECTION; PE61101E; PROBABILISTIC CONTEXT FREE GRAMMAR; SEMANTIC INFORMATION REPRESENTATION; SEMANTICS; SENSOR DATA; SENSOR UTILITY METRICS; SPARSE CODING; VISUAL SENSING; WUAFRL1000Y02C
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA589989
http://www.dtic.mil/docs/citations/ADA589989
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15
Detecting grammatical errors with treebank-induced, probabilistic parsers
Wagner, Joachim. - : Dublin City University. School of Computing, 2012
In: Wagner, Joachim orcid:0000-0002-8290-3849 (2012) Detecting grammatical errors with treebank-induced, probabilistic parsers. PhD thesis, Dublin City University. (2012)
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16
Techniques for utterance disambiguation in a human-computer dialogue system
Lurcock, Pontus Conrad. - : University of Otago, 2012
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17
Linguistic nativism and the poverty of the stimulus
Lappin, Shalom; Clark, Alexander. - Malden, Mass. [u.a.] : Wiley-Blackwell, 2011
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UB Frankfurt Linguistik
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18
Integration of Data from a Syntactic Lexicon into Generative and Discriminative Probabilistic Parsers
In: International conference on Recent Advances in Natural Language Processing (RANLP'11) ; https://hal-upec-upem.archives-ouvertes.fr/hal-00621646 ; International conference on Recent Advances in Natural Language Processing (RANLP'11), 2011, Hissar, Bulgaria. pp.363-370 (2011)
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19
Incremental Syntactic Language Models for Phrase-Based Translation
In: DTIC (2011)
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20
From Exemplar to Grammar: A Probabilistic Analogy-based Model of Language Learning
In: http://staff.science.uva.nl/~rens/analogy.pdf (2009)
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