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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 – 5 of 5 1 Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level ... Elmadany, AbdelRahim; Abdou, Sherif; Gheith, Mervat. - : arXiv, 2018 BASE Show details 2 JANA: A Human-Human Dialogues Corpus for Egyptian Dialect Elmadany, AbdelRahim A.; Abdou, Sherif M.; Gheith, Mervat. - : Linguistic Data Consortium, 2016. : https://www.ldc.upenn.edu, 2016 BASE Show details 3 JANA: A Human-Human Dialogues Corpus for Egyptian Dialect ... Elmadany, AbdelRahim A.; Abdou, Sherif M.; Gheith, Mervat. - : Linguistic Data Consortium, 2016 BASE Show details 4 Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and Instance Messages ... Elmadany, AbdelRahim A.; Abdou, Sherif M.; Gheith, Mervat. - : arXiv, 2015 BASE Show details 5 Towards Understanding Egyptian Arabic Dialogues ... Elmadany, Abdelrahim A; Abdou, Sherif M; Gheith, Mervat. - : arXiv, 2015 Abstract: Labelling of user's utterances to understanding his attends which called Dialogue Act (DA) classification, it is considered the key player for dialogue language understanding layer in automatic dialogue systems. In this paper, we proposed a novel approach to user's utterances labeling for Egyptian spontaneous dialogues and Instant Messages using Machine Learning (ML) approach without relying on any special lexicons, cues, or rules. Due to the lack of Egyptian dialect dialogue corpus, the system evaluated by multi-genre corpus includes 4725 utterances for three domains, which are collected and annotated manually from Egyptian call-centers. The system achieves F1 scores of 70. 36% overall domains. ... : arXiv admin note: substantial text overlap with arXiv:1505.03081 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1509.03208 https://arxiv.org/abs/1509.03208 BASE Hide details

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