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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Automatic speech recognition and machine translation of Arabic and dialectal videos ; Reconnaissance et traduction automatique de la parole de vidéos arabes et dialectales Menacer, Mohamed Amine. - : HAL CCSD, 2020 In: https://hal.univ-lorraine.fr/tel-03132934 ; Informatique et langage [cs.CL]. Université de Lorraine, 2020. Français. ⟨NNT : 2020LORR0157⟩ (2020) BASE Show details 2 Linguistic Analysis and Automatic Information Extraction of Semantic Relations in Arabic ; Analyse linguistique et extraction automatique de relations sémantiques des textes en arabe MORSI, Youcef Ihab. - : HAL CCSD, 2020 In: https://hal.archives-ouvertes.fr/tel-03572307 ; Linguistique. Université Bourgogne Franche-Comté, 2020. Français (2020) BASE Show details 3 An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes ... Alshammari, Nasser; Alanazi, Saad. - : Zenodo, 2020 BASE Show details 4 An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes ... Alshammari, Nasser; Alanazi, Saad. - : Zenodo, 2020 Abstract: This is a novel data descriptor that provides the Arabic natural language processing community with a dataset dedicated to named entity recognition tasks for diseases. The dataset comprises more than 60 thousand words, which were annotated manually by two independent annotators using the inside-outside (IO) annotation scheme. To ensure the reliability of the annotation process, the inter-annotator agreements rate was calculated, and it scored 95.14\%. Due to the lack of research efforts in the literature dedicated for studying Arabic multi-annotation schemes, a distinguishing and a novel aspect of this dataset is the inclusion of six more annotation schemes that will bridge the gap by allowing the researchers to explore and compare the effects of these schemes on the performance of the Arabic named entity recognizers. These annotation schemes are IOE, IOB, BIES, IOBES, IE, and BI. Additionally, five linguistic features, including part-of-speech tags, stopwords, gazetteers, lexical markers, and the presence ... Keyword: Named Entity Recognition; Modern Standard Arabic Corpus; Annotation Schemes URL: https://dx.doi.org/10.5281/zenodo.3926431 https://zenodo.org/record/3926431 BASE Hide details 5 Remarks on Modern Standard Arabic Construct State and Quantification Abuhaib, Mohammed In: Theses and Dissertations (2020) BASE Show details 6 THE PLACE DEIXIS OF MODERN STANDARD ARABIC: A CLOSER LOOK AT THE DIMENSIONAL SYSTEM AND THE FACTORS THAT CONTROL THE CHOICE OF PLACE DEICTIC EXPRESSIONS Alluhaybi, Ibrahim A.. - 2020 BASE Show details

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