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Measuring the Reading-Attention Relationship: Functional Differences in Working Memory Activity During Single Word Decoding in Children With and Without Reading Disorder
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| 2 |
Acquisition of Joint Attention Skills in Children with Cortical Visual Impairment
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| 3 |
Speaking Style Variability in Speaker Discrimination by Humans and Machines
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| 4 |
Evidence from ERP and Eye Movements as Markers of Language Dysfunction in Dyslexia
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In: ISSN: 2076-3425 ; Brain Sciences ; https://hal.archives-ouvertes.fr/hal-03641338 ; Brain Sciences, MDPI, 2022, 12 (1), pp.73. ⟨10.3390/brainsci12010073⟩ (2022)
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| 6 |
Working memory and domain in DLD (Larson & Ellis Weismer, 2022) ...
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| 7 |
Working memory and domain in DLD (Larson & Ellis Weismer, 2022) ...
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| 8 |
白噪音對注意力不足過動症學童課堂注意力表現之影響 ; Effects of White Noise on Attention in the Classroom for Children with ADHD
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| 9 |
Memory and linguistic demands of the Token Test (Pham et al., 2022) ...
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| 10 |
Memory and linguistic demands of the Token Test (Pham et al., 2022) ...
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| 13 |
Effects of Spatial Speech Presentation on Listener Response Strategy for Talker-Identification ...
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| 14 |
Attention-Language Interface in Multilingual Assessment Instrument for Narratives (MAIN) ...
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| 15 |
Attention-Language Interface in Multilingual Assessment Instrument for Narratives (MAIN) ...
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| 16 |
Effects of CPAP on Language Recovery in Post-Stroke Aphasia: A Review of Recent Literature
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In: Brain Sciences; Volume 12; Issue 3; Pages: 379 (2022)
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| 17 |
A Character String-Based Stemming for Morphologically Derivative Languages
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In: Information; Volume 13; Issue 4; Pages: 170 (2022)
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Abstract:
Morphologically derivative languages form words by fusing stems and suffixes, stems are important to be extracted in order to make cross lingual alignment and knowledge transfer. As there are phonetic harmony and disharmony when linguistic particles are combined, both phonetic and morphological changes need to be analyzed. This paper proposes a multilingual stemming method that learns morpho-phonetic changes automatically based on character based embedding and sequential modeling. Firstly, the character feature embedding at the sentence level is used as input, and the BiLSTM model is used to obtain the forward and reverse context sequence, and the attention mechanism is added to this model for weight learning, and the global feature information is extracted to capture the stem and affix boundaries; finally CRF model is used to learn more information from sequence features to describe context information more effectively. In order to verify the effectiveness of the above model, the model in this paper is compared with the traditional model on two different data sets of three derivative languages: Uyghur, Kazakh and Kirghiz. The experimental results show that the model in this paper has the best stemming effect on multilingual sentence-level datasets, which leads to more effective stemming. In addition, the proposed model outperforms other traditional models, and fully consider the data characteristics, and has certain advantages with less human intervention.
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Keyword:
agglutinative language; attention mechanism; BiLSTM-Attention-CRF; context; multilingual language; stemming
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URL: https://doi.org/10.3390/info13040170
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| 18 |
A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis
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In: Mathematics; Volume 10; Issue 6; Pages: 914 (2022)
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| 19 |
What Is Going on with Visual Attention in Reading and Dyslexia? A Critical Review of Recent Studies
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In: Brain Sciences; Volume 12; Issue 1; Pages: 87 (2022)
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| 20 |
Should Neurosurgeons Try to Preserve Non-Traditional Brain Networks? A Systematic Review of the Neuroscientific Evidence
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In: Journal of Personalized Medicine; Volume 12; Issue 4; Pages: 587 (2022)
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