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Distributional Semantic Models for English verbs and nouns ...
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The Role of negative information when learning dense word vectors ; O papel da informação negativa na aprendizagem de vetores palavra densos
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Word Representations Concentrate and This is Good News!
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In: CoNLL 2020 - 24th Conference on Computational Natural Language Learning ; https://hal.univ-grenoble-alpes.fr/hal-03356609 ; CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Association for Computational Linguistics (ACL), Nov 2020, Online, France. pp.325-334, ⟨10.18653/v1/2020.conll-1.25⟩ (2020)
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Can word vectors help corpus linguists? ; Les vecteurs lexicaux peuvent-ils venir en aide aux linguistes de corpus ?
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In: ISSN: 0039-3274 ; Studia Neophilologica ; https://halshs.archives-ouvertes.fr/halshs-01657591 ; Studia Neophilologica, Taylor & Francis (Routledge): SSH Titles, 2019, ⟨10.1080/00393274.2019.1616220⟩ (2019)
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Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
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In: Applied Sciences ; Volume 9 ; Issue 18 (2019)
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RedMed: Extending drug lexicons for social media applications ...
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RedMed: Extending drug lexicons for social media applications ...
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Sparse distributed representations as word embeddings for language understanding
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Using EEG to decode semantics during an artificial language learning task
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Abstract:
The study of semantics in the brain explores how the brain represents, processes, and learns the meaning of language. In this thesis we show both that semantic representations can be decoded from electroencephalography data, and that we can detect the emergence of semantic representations as participants learn an artificial language mapping. We collected electroencephalography data while participants performed a reinforcement learning task that simulates learning an artificial language, and then developed a machine learning semantic representation model to predict semantics as a word-to-symbol mapping was learned. Our results show that 1) we can detect a reward positivity when participants correctly identify a symbol's meaning; 2) the reward positivity diminishes for subsequent correct trials; 3) we can detect neural correlates of the semantic mapping as it is formed; and 4) the localization of the neural representations is heavily distributed. Our work shows that language learning can be monitored using EEG, and that the semantics of even newly-learned word mappings can be detected using EEG. ; Graduate
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Keyword:
EEG; language learning; reward positivity; semantic representation; word semantics; word vectors
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URL: https://dspace.library.uvic.ca//handle/1828/10382
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Construction grammars: the empirical challenge ; Les grammaires de constructions à l'épreuve de l'empirie
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In: https://halshs.archives-ouvertes.fr/tel-01657598 ; Linguistique. Université Paris Diderot (Paris 7), 2016 (2016)
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A Statistical Approach to Retrieving Historical Manuscript Images without Recognition
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In: DTIC (2003)
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disambiguation
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In: http://lexicometrica.univ-paris3.fr/jadt/jadt2012/Communications/Maldonado-Guerra+et+al.+-+First-order+and+second-order+context+representations.pdf
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Use of Semantic Relation Between Words in Text Clustering
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In: http://www.cse.iitb.ac.in/~pb/papers/cluster_unl.pdf
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