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Morphology in the Corsican Language Database (BDLC) : assessment and perspectives ; La morphologie dans la Banque de Données Langue Corse : bilan et perspectives
In: ISSN: 1638-9808 ; EISSN: 1765-3126 ; Corpus ; https://hal.archives-ouvertes.fr/hal-03591866 ; Corpus, Bases, Corpus, Langage - UMR 7320, 2022, Corpus et données en morpholgie, ⟨10.4000/corpus.7115⟩ ; https://journals.openedition.org/corpus/7115 (2022)
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Computational models of disfluencies : fillers and discourse markers in spoken language understanding ; Modèles computationnels des disfluences dans le traitement de la parole
Dinkar, Tanvi. - : HAL CCSD, 2022
In: https://tel.archives-ouvertes.fr/tel-03653211 ; Computer science. Institut Polytechnique de Paris, 2022. English. ⟨NNT : 2022IPPAT001⟩ (2022)
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Word Sense Induction with Attentive Context Clustering
In: https://hal.archives-ouvertes.fr/hal-03586559 ; 2022 (2022)
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Word Sense Induction with Attentive Context Clustering
In: https://hal.inria.fr/hal-03586559 ; 2022 (2022)
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Word Sense Induction with Attentive Context Clustering
In: https://hal.archives-ouvertes.fr/hal-03586559 ; 2022 (2022)
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Korrelate gelingender Interpersonalität bei Patient:innen mit nicht-affektiven Psychosen ... : Correlates of successful interpersonality in non-affective psychosis ...
Just, Sandra Anna. - : Charité - Universitätsmedizin Berlin, 2022
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7
Grenzüberschreitendes Textmining von Historischen Zeitungen - Das impresso-Projekt zwischen Text- und Bildverarbeitung, Design und Geschichtswissenschaft ...
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Grenzüberschreitendes Textmining von Historischen Zeitungen - Das impresso-Projekt zwischen Text- und Bildverarbeitung, Design und Geschichtswissenschaft ...
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9
LinkingPark: Automatic Semantic Table Interpretation Software ...
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10
LivingNER corpus: Named entity recognition, normalization & classification of species, pathogens and food ...
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LinkingPark: Automatic Semantic Table Interpretation Software ...
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LivingNER corpus: Named entity recognition, normalization & classification of species, pathogens and food ...
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13
DisTEMIST corpus: detection and normalization of disease mentions in spanish clinical cases ...
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DisTEMIST corpus: detection and normalization of disease mentions in spanish clinical cases ...
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15
INTEGRATION OF PHONOTACTIC FEATURES FOR LANGUAGE IDENTIFICATION ON CODE-SWITCHED SPEECH ...
Koena Mabokela. - : Zenodo, 2022
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INTEGRATION OF PHONOTACTIC FEATURES FOR LANGUAGE IDENTIFICATION ON CODE-SWITCHED SPEECH ...
Koena Mabokela. - : Zenodo, 2022
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17
Sentence Level Embedding Detoxification via Toxic Component Removal ...
: University of Virginia, 2022
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18
MULDASA: Multifactor Lexical Sentiment Analysis of Social-Media Content in Nonstandard Arabic Social Media
In: Applied Sciences; Volume 12; Issue 8; Pages: 3806 (2022)
Abstract: The semantically complicated Arabic natural vocabulary, and the shortage of available techniques and skills to capture Arabic emotions from text hinder Arabic sentiment analysis (ASA). Evaluating Arabic idioms that do not follow a conventional linguistic framework, such as contemporary standard Arabic (MSA), complicates an incredibly difficult procedure. Here, we define a novel lexical sentiment analysis approach for studying Arabic language tweets (TTs) from specialized digital media platforms. Many elements comprising emoji, intensifiers, negations, and other nonstandard expressions such as supplications, proverbs, and interjections are incorporated into the MULDASA algorithm to enhance the precision of opinion classifications. Root words in multidialectal sentiment LX are associated with emotions found in the content under study via a simple stemming procedure. Furthermore, a feature–sentiment correlation procedure is incorporated into the proposed technique to exclude viewpoints expressed that seem to be irrelevant to the area of concern. As part of our research into Saudi Arabian employability, we compiled a large sample of TTs in 6 different Arabic dialects. This research shows that this sentiment categorization method is useful, and that using all of the characteristics listed earlier improves the ability to accurately classify people’s feelings. The classification accuracy of the proposed algorithm improved from 83.84% to 89.80%. Our approach also outperformed two existing research projects that employed a lexical approach for the sentiment analysis of Saudi dialects.
Keyword: Arabic NLP; Arabic social media; lexical; Saudi dialects; sentiment analysis; Twitter
URL: https://doi.org/10.3390/app12083806
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19
Semantic Feature Extraction Using SBERT for Dementia Detection
In: Brain Sciences; Volume 12; Issue 2; Pages: 270 (2022)
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20
Sentence Boundary Extraction from Scientific Literature of Electric Double Layer Capacitor Domain: Tools and Techniques
In: Applied Sciences; Volume 12; Issue 3; Pages: 1352 (2022)
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