Year: 2019
Type: conference-paper
Abstract: This paper presents the results of the experiments done as a part of MADAR Shared Task in WANLP 2019 on Arabic Fine-Grained Dialect Identification.Dialect Identification is one of the prominent tasks in the field of Natural language processing where the subsequent language modules can be improved based on it.We explored the use of different features like char, word n-gram, language model probabilities, etc on different classifiers.Results show that these features help to improve dialect classification accuracy.Results also show that traditional machine learning classifier tends to perform better when compared to neural network models on this task in a low resource setting.
DOI: 10.18653/v1/w19-4628
Authors: Pruthwik Mishra, Vandan Mujadia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2971054893