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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)
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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)
Abstract: While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase-structure trees should be assigned to initial sentences, s ⁄ he allows (implicitly) for all possible trees and lets linguistic experience decide which is the ‘‘best’ ’ tree for each sentence. The best tree is obtained by maximizing ‘‘structural analogy’ ’ between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus-based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar-based and rule-based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children’s language development from item-based constructions to abstract constructions, and that the model can simulate some of the errors made by children
Keyword: Analogy; Auxiliary fronting; Computational modeling; Constructions; Data-oriented parsing (DOP; Discontiguous dependencies; Distituents; Language acquisition; Language generation; Poverty of the stimulus; Probabilistic context-free grammar; Probabilistic tree-substitution grammar; Rules versus exemplars; Statistical grammar induction; Unsupervised parsing; Unsupervised-DOP
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.8237
http://staff.science.uva.nl/~rens/analogy.pdf
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