Machine Learning-Based Approach for A rabic Dialect Identification (2021)

Abstract: This paper describes our systems submitted to the Second Nuanced Arabic Dialect Identification Shared Task (NADI 2021). Dialect identification is the task of automatically detecting the source variety of a given text or speech segment. There are four subtasks, two subtasks for country-level identification and the other two subtasks for province-level identification. The data in this task covers a total of 100 provinces from all 21 Arab countries and come from the Twitter domain. The proposed systems depend on five machine-learning approaches namely Complement Naïve Bayes, Support Vector Machine, Decision Tree, Logistic Regression and Random Forest Classifiers. F1 macro-averaged score of Naïve Bayes classifier outperformed all other classifiers for development and test data.

## introduction today, huge amounts of data in text, picture, and video format are posted to social network sites, the general web, and mobile devices. social networks like twitter are based on interactions with fast temporal dynamics which generate a large variety of contents with their own characteristics which are difficult to compute with classical tools used on traditional texts like essays and articles @xcite . this article focuses on dialect identification, which is a technology critical in tasks such as author profiling and other nlp downstream tasks such as sentiment analysis, pos tagging, text summarization, among others. in dialect identification, we face some questions: how to find differences in writing style on social networks between men and women, age groups, or location. the answers to these questions are important for the new problem we face in the era of social networks such as fake news, plagiarism, and identity theft @xcite . recently, research community concerning arabic natural language processing focussed on dialect identification for arabic @xcite @xcite . a shared task for nuanced arabic dialect identification (nadi 2020) has been organized to identify the dialect in arabic tweets. we propose five models to perform dialect identification for arabic tweets. the term frequency inverse document frequency (tf/idf) algorithm is implemented for feature extraction after preprocessing process. complement naïve bayes (cnb), decision tree (dt), logistic regression (lr), random forest (rf), and support vector machine (svm) classifiers are used in the classification step. the results of development set showing that cnb classifier outperforms all other classifiers. ## data the nadi 2021 datasets were the main corpus used for training and testing the system @xcite . the nadi 2021 datasets are in both modern standard arabic (msa) and dialectal arabic (da). for each of these varieties, the dataset is partitioned into three parts, the training part of 21,000 tweets, development of 5,000 tweets, and test set of 5,000 tweets the data sets are labeled in two levels: the first level (country level) of 21 countries and the second level (provinces level) of 100 provinces. the test set was published unlabelled, and the system output was evaluated by the nadi shared task team. the training set was used to train our models while the development set was used to optimize models' parameters. the distribution of data over the country and province classes is unbalanced. ## system architecture as shown in figure 1 , our system composed of four main stages. these are: text preprocessing, feature extraction, classification, evaluation and testing. text preprocessing, where we make some cleaning and corrections on input as we will explain next. in the second stage important features from all documents have been selected. the third stage is classification, in which we classify each vector in the feature vectors to its correct class (correct arabic dialect). the model has been tested in evaluation stage. at last, a blind test set has been used to predict the correct class (arabic dialect). ## results we propose five machine learning algorithms for the task: cnb, dt, lr, rf and svm classifiers. all algorithms were implemented on nadi shared task data set. there are four subtask: 1.1 for msa-country-level, 1.2 for da-country-level, 2.1 for msa-province-level, and 2.2 for da-provincelevel. the f 1 score is the evaluation metric for all subtasks. the complement naïve bayes (cnb) classifier outperformed all other classifiers in all subtasks and achieved the highest f 1 -score ratio for the development set data. the results of test set is given in ## discussion we provided a description of our different models we developed for nadi 2021. the dataset of 21 country-level classes and 100 province-level classes were used to evaluate the systems. there ## conclusion we can conclude that arabic dialect identification is one of the most challenging tasks for many reasons mentioned. this paper proposed five classifier models for identifying arabic dialect in twitter. the results of training using complement naïve bayes classifier achieved the best f 1 macroaveraged score as it is very good to deal with nlp depending on naïve bayes rule. in future work, different weighting scores can be used to improve the performance of classification, such as word embeddings.