Arabic Dialect Identification for Travel and Twitter Text

Proceedings of the Fourth Arabic Natural Language Processing Workshop

Arabic dialect identification is an inherently complex problem, as Arabic dialect taxonomy is convoluted and aims to dissect a continuous space rather than a discrete one. In this work, we present machine and deep learning approaches to predict 21 fine-grained dialects form a set of given tweets per user. We adopted numerous feature extraction methods most of which showed improvement in the final model, such as word embedding, Tf-idf, and other tweet features. Our results show that a simple LinearSVC can outperform any complex deep learning model given a set of curated features. With a relatively complex user voting mechanism, we were able to achieve a Macro-Averaged F1-score of 71.84% on MADAR shared subtask-2. Our best submitted model ranked second out of all participating teams.

Dialect identification is a prior requirement for learning lexical and morphological knowledge a language variation that can be beneficial for natural language processing (NLP) and potential AI downstream tasks. In this paper, we present the first work on sentence-level Modern Standard Arabic (MSA) and Saudi Dialect (SD) identification where we trained and tested three classifiers (Logistic regression, Multi-nominal Na¨ıve Bayes, and Support Vector Machine) on datasets collected from Saudi Twitter and automatically labeled as (MSA) or SD. The model for each configuration was built using two levels of language models, i.e., unigram and bi-gram, as feature sets for training the systems. The model reported high-accuracy performance using 10-fold cross-validations with average 98.98%. This model was evaluated on another unseen, manually-annotated dataset. The best performance of these classifiers was achieved by Multi-nominal Naïve Bayes, reporting 89%.

Proceedings of the Fourth Arabic Natural Language Processing Workshop

In this paper, we present a Dialect Identification system (ArbDialectID) that competed at Task 1 of the MADAR shared task, MADAR Travel Domain Dialect Identification. We build a coarse and a fine grained identification model to predict the label (corresponding to a dialect of Arabic) of a given text. We build two language models by extracting features at two levels (words and characters). We firstly build a coarse identification model to classify each sentence into one out of six dialects, then use this label as a feature for the fine grained model that classifies the sentence among 26 dialects from different Arab cities, after that we apply ensemble voting classifier on both subsystems. Our system ranked 1st that achieving an f-score of 67.32%. Both the models and our feature engineering tools are made available to the research community.

Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)

This paper describes the systems submitted by BFCAI team to Nuanced Arabic Dialect Identification (NADI) shared task 2022. Dialect identification task aims at detecting the source variant of a given text or speech segment automatically. There are two subtasks in NADI 2022, the first subtask for country-level identification and the second subtask for sentiment analysis. Our team participated in the first subtask. The proposed systems use Term Frequency Inverse/Document Frequency and word embeddings as vectorization models. Different machine learning algorithms have been used as classifiers. The proposed systems have been tested on two test sets: Test-A and Test-B. The proposed models achieved Macro-f1 score of 21.25% and 9.71% for Test-A and Test-B set respectively. On other hand, the best-performed submitted system achieved Macro-f1 score of 36.48% and 18.95% for Test-A and Test-B set respectively.

Arabic Language Processing: From Theory to Practice, 2019

This research deals with Arabic dialect identification, a challenging issue related to Arabic NLP. Indeed, the increasing use of Arabic dialects in a written form especially in social media generates new needs in the area of Arabic dialect processing. For discriminating between dialects in a multi-dialect context, we use different approaches based on machine learning techniques. To this end, we explored several methods. We used a classification method 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.

Proceedings of the Fourth Arabic Natural Language Processing Workshop

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%).

Proceedings of the Fourth Arabic Natural Language Processing Workshop, 2019

Studies on Dialectical Arabic are growing more important by the day as it becomes the primary written and spoken form of Arabic online in informal settings. Among the important problems that should be explored is that of dialect identification. This paper reports different techniques that can be applied towards such goal and reports their performance on the Multi Arabic Dialect Applications and Resources (MADAR) Arabic Dialect Corpora. Our results show that improving on traditional systems using frequency based features and non deep learning classifiers is a challenging task. We propose different models based on different word and document representations. Our top model is able to achieve an F1 macro averaged score of 65.66 on MADAR's smallscale parallel corpus of 25 dialects and Modern Standard Arabic (MSA).

2020

Around the Arab world, different Arabic dialects are spoken by more than 300M persons, and are increasingly popular in social media texts. However, Arabic dialects are considered to be low-resource languages, limiting the development of machine-learning based systems for these dialects. In this paper, we investigate the Arabic dialect identification task, from two perspectives: country-level dialect identification from 21 Arab countries, and province-level dialect identification from 100 provinces. We introduce an unified pipeline of state-of-the-art models, that can handle the two subtasks. Our experimental studies applied to the NADI shared task under the team name BERT-NGRAMS, show promising results both at the country-level (F1-score of 25.99%) and the province-level (F1-score of 6.39%), and thus allow us to be ranked 2nd for the country-level subtask, and 1st in the province-level subtask.

2019

In recent years, research in Natural Language Processing (NLP) on Arabic has garnered significant attention. This includes research about classification of Arabic dialect texts, but due to the lack of Arabic dialect text corpora this research has not achieved a high accuracy. Arabic dialects text classification is becoming important due to the increasing use of Arabic dialect in social media, so this text is now considered quite appropriate as a medium of communication and as a source of a corpus. We collected tweets, comments from Facebook and online newspapers representing five groups of Arabic dialects: Gulf, Iraqi, Egyptian, Levantine, and North African. This paper investigates how to classify Arabic dialects in text by extracting lexicons for each dialect which show the distinctive vocabulary differences between dialects. We describe the lexicon-based methods used to classify Arabic dialect texts and present the results, in addition to techniques used to improve accuracy.

arXiv (Cornell University), 2023

In this paper, we conduct an in-depth analysis of several key factors influencing the performance of Arabic Dialect Identification NADI'2023, with a specific focus on the first subtask involving country-level dialect identification. Our investigation encompasses the effects of surface preprocessing, morphological preprocessing, FastText vector model, and the weighted concatenation of TF-IDF features. For classification purposes, we employ the Linear Support Vector Classification (LSVC) model. During the evaluation phase, our system demonstrates noteworthy results, achieving an F1 score of 62.51%. This achievement closely aligns with the average F1 scores attained by other systems submitted for the first subtask, which stands at 72.91%.