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

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秘密會議

自作多情

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.

Nostalgia

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.

執(zhí)

ABYSS

暗語

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.

無孔

forthy

prosperity