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Word Order, Intonation, and Prosodic Phrasing: Individual Differences in the Production and Identification of Narrow and Wide Focus in Urdu
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In: Languages; Volume 7; Issue 2; Pages: 103 (2022)
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| 2 |
Word Order, Intonation, and Prosodic Phrasing: Individual Differences in the Production and Identification of Narrow and Wide Focus in Urdu
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Predicting lexical complexity in English texts: the Complex 2.0 dataset
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Automatic Loanword Identification Using Tree Reconciliation ...
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WORD IDENTIFICATION IN THE CONTEXT ... : ИДЕНТИФИКАЦИЯ СЛОВ В КОНТЕКСТЕ ...
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| 10 |
The Cross-Modal Suppressive Role of Visual Context on Speech Intelligibility: An ERP Study.
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In: Brain sciences, vol 10, iss 11 (2020)
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| 11 |
The Cross-Modal Suppressive Role of Visual Context on Speech Intelligibility: An ERP Study
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In: Brain Sciences ; Volume 10 ; Issue 11 (2020)
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| 12 |
Identificação de comentários ofensivos da Web ; Identification of offensive comments on the web
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| 13 |
Atribuição autoral de textos digitais ; Authorship Attribution of digital texts
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Custódio, José Eleandro. - : Biblioteca Digital de Teses e Dissertações da USP, 2019. : Universidade de São Paulo, 2019. : Escola de Artes, Ciências e Humanidades, 2019
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| 14 |
Relations entre lecture et vocabulaire chez des élèves ayant un trouble développemental du langage de 8 à 11 ans
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| 15 |
Phoneme Resistance and Phoneme Confusion in Noise: Impact of Dyslexia
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In: Proceedings of Interspeech 2018 ; Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-01961066 ; Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association, Sep 2018, Hyperabad, India. pp.2290-2294, ⟨10.21437/interspeech.2018-1271⟩ ; http://interspeech2018.org/ (2018)
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| 17 |
Aggressive language identification using word embeddings and sentiment features
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Abstract:
This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment analyser. Several machine learning methods and different combinations of features were tried. The official submissions used Support Vector Machines and Random Forests. The official evaluation showed that for texts similar to the ones in the training dataset Random Forests work best, whilst for texts which are different SVMs are a better choice. The evaluation also showed that despite its simplicity the method performs well when compared with more elaborated methods.
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Keyword:
aggression identification; sentiment analysis; word embeddings
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URL: http://hdl.handle.net/2436/621749
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| 18 |
國小六年級學生對數學教科書文字題詞彙識別之研究 ; A Study on the Vocabulary Identification of Word Problems in Mathematics Textbooks for 6th Grade Students
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| 19 |
Encouraging Word Identification Competencies Among Developing Readers
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In: ETSU Faculty Works (2017)
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| 20 |
Disentangling stimulus plausibility and contextual congruency: Electrophysiological evidence for differential cognitive dynamics
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