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Non-adjacent dependency learning over consonants & vowels in 8- to 10-month-olds ...
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Elucidating an implicit learning network in healthy adults during artificial grammar tasks
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In: Master's Theses and Capstones (2021)
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Information flow, artificial phonology and typology
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Perceptual saliency, lenition, and learnability: An artificial grammar learning study
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics
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In: Computer Science: Faculty Publications and Other Works (2020)
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Abstract:
A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modelling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited–bootstrapping from the features returned by Word Embedding mechanisms–to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.
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Keyword:
Applied Linguistics; Brain-Inspired Artificial Neural Networks; computational linguistics; Computer Sciences; cortical dynamics; Grammar Emergence; Online sentence processing; Psycholinguistics and Neurolinguistics; unsupervised learning
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URL: https://ecommons.luc.edu/cs_facpubs/240 https://www.frontiersin.org/articles/10.3389/fncir.2020.00012/abstract
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Conversational artificial intelligence - demystifying statistical vs linguistic NLP solutions
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Bias in Phonotactic Learning: Experimental Studies of Phonotactic Implicationals
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In: Glewwe, Eleanor. (2019). Bias in Phonotactic Learning: Experimental Studies of Phonotactic Implicationals. UCLA: Linguistics 0510. Retrieved from: http://www.escholarship.org/uc/item/4456s1j0 (2019)
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Inductive learning of locality relations in segmental phonology
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 10, No 1 (2019); 14 ; 1868-6354 (2019)
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics ...
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The role of auditory perceptual gestalts on the processing of phrase structure ...
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The ambiguous status of laryngeals in nasal vowel-consonant harmony
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In: Toronto Working Papers in Linguistics; Vol 40 (2018): Special issue from the CRC-sponsored phonology/phonetics workshops ; 1718-3510 ; 1705-8619 (2018)
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On the Relation between Phonotactic Learning and Alternation Learning
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In: Chong, Junxiang Adam. (2017). On the Relation between Phonotactic Learning and Alternation Learning. UCLA: Linguistics 0510. Retrieved from: http://www.escholarship.org/uc/item/7235q340 (2017)
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It Doesn't Hurt to Try: The Impact of a Search for Structure in Artificial Grammar Learning
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A Supervised Approach for Enriching the Relational Structure of Frame Semantics in FrameNet
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In: Proceedings of COLING 2016 ; 26th International Conference on Computational Linguistics (COLING 2016) ; https://hal.archives-ouvertes.fr/hal-01709130 ; 26th International Conference on Computational Linguistics (COLING 2016), Dec 2016, Osaka, Japan. pp. 3542-3552 (2016)
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Simple K-star Categorial Dependency Grammars and their Inference
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In: The 13th International Conference on Grammatical Inference (ICGI) ; https://hal.archives-ouvertes.fr/hal-01363393 ; The 13th International Conference on Grammatical Inference (ICGI), Oct 2016, Delft, Netherlands ; http://icgi2016.tudelft.nl/ (2016)
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Surface Realisation from Knowledge Bases ; Bases de Connaissances et Réalisation de Surface
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In: https://hal.inria.fr/tel-01754499 ; Computation and Language [cs.CL]. Université de Lorraine, 2016. English. ⟨NNT : 2016LORR0004⟩ (2016)
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What Matters in Artificial Learning, Sonority Hierarchy or Natural Classes?
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2015 Annual Meeting on Phonology ; 2377-3324 (2016)
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