Search Results for author:
Found 3 papers, 0 papers with code
Arabic Dialect Identification based on a Weighted Concatenation of TF-IDF Features
In this paper, we analyze the impact of the weighted concatenation of TF-IDF features for the Arabic Dialect Identification task while we participated in the NADI2021 shared task.
Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic
The findings show that despite the simplicity of the proposed approach, using the LSVC model with a normalizing Arabic (NA) preprocessing and the BiLSTM architecture with an Embedding layer as input have yielded an encouraging F1score of 33. 71% and 57. 80% for sarcasm and sentiment detection, respectively.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
