Titlebook: Arabic Language Processing: From Theory to Practice; 7th International Co Kamel Smaïli Conference proceedings 2019 Springer Nature Switzerl

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书目名称Arabic Language Processing: From Theory to Practice影响因子(影响力)

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书目名称Arabic Language Processing: From Theory to Practice网络公开度学科排名

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书目名称Arabic Language Processing: From Theory to Practice被引频次学科排名

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书目名称Arabic Language Processing: From Theory to Practice年度引用学科排名

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书目名称Arabic Language Processing: From Theory to Practice读者反馈学科排名

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Automatic Identification Methods on a Corpus of Twenty Five Fine-Grained Arabic Dialectsethod based on symmetric Kullback-Leibler, and we experimented classical classification methods such as Naive Bayes Classifiers and more sophisticated methods like Word2Vec and Long Short-Term Memory neural network. We tested our approaches on a large database of 25 Arabic dialects in addition to MSA.

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Sentiment Analysis of Code-Switched Tunisian Dialect: Exploring RNN-Based Techniqueslect, which is characterized both by a frequent occurring of code-switching and by a double script (Arabic and Latin) when written on the social media. Our study aims to explore and compare various classification models based on RNNs (Recurrent Neural Networks), precisely on LSTM (Long Short-Term Memory) neural networks.

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Experimental Infections by Spirilla,IS (Chist-Era project), we collected a corpus of 1503 Arabic and 1874 English videos. These videos need to be aligned in order to compare their contents, that is why we propose several methods to make them comparable. Then the best one is selected to align them and to constitute the data-set necessary for the fine-grained sentiment analysis.

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