Work

ST MADAR 2019 Shared Task: Arabic Fine-Grained Dialect Identification

Year: 2019

Type: conference-paper

Abstract: This paper describes the solution that we propose on MADAR 2019 Arabic Fine-Grained Dialect Identification task.The proposed solution utilized a set of classifiers that we trained on character and word features.These classifiers are: Support Vector Machines (SVM), Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), Stochastic Gradient Descent (SGD), Passive Aggressive(PA) and Perceptron (PC).The system achieved competitive results, with a performance of 62.87% and 62.12% for both development and test sets.

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.18653/v1/w19-4635

Authors: Mourad Abbas, Mohamed Lichouri, Abed Alhakim Freihat

Institution: University of Trento

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2970011044

FWCI: 0.574

Cites: 13

Cited by: 11

Related to: 10