Work

MICHAEL: Mining Character-level Patterns for Arabic Dialect Identification (MADAR Challenge)

Year: 2019

Type: conference-paper

Abstract: We present MICHAEL, a lightweight method developed for the MADAR shared task on travel domain Dialect Identification (DID).It uses character-level features and perform classification without any pre-processing.Character N-grams extracted from the original sentences are used to train a Multinomial Naive Bayes classifier.MICHAEL achieved an official score (accuracy) of 53.25% with 1 ≤ N ≤ 3 but showed a much better result with character 4-grams (62.17%).

Source: Arabic Natural Language Processing Workshop (WANLP)

DOI: 10.18653/v1/w19-4627

Authors: Dhaou Ghoul, Gaël Lejeune

Institutions: Université Stendhal – Grenoble 3, Sorbonne Université, Sens, Texte, Informatique, Histoire

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2971248512

FWCI: 0.574

Cites: 14

Cited by: 8

Related to: 10