DALILA: The Dialectal Arabic Linguistic Learning Assistant

International audienceDialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP). The number and sophistication of tools and datasets in DA are very limited in comparison to Modern Standard Arabic (MSA) and other languages. MSA tools do not effectively model DA which makes the direct use of MSA NLP tools for handling dialects impractical. This is particularly a challenge for the creation of tools to support learning Arabic as a living language on the web, where authentic material can be found in both MSA and DA. In this paper, we present the Dialectal Arabic Linguistic Learning Assistant (DALILA), a Chrome extension that utilizes cutting-edge Arabic dialect NLP research to assist learners and non-native speakers in understanding text written in either MSA or DA. DALILA provides dialectal word analysis and English gloss corresponding to each word

Arabic tweeps dialect prediction based on machine learning approach

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector

Classification of colloquial Arabic tweets in real-time to detect high-risk floods

Twitter has eased real-time information flow for decision makers, it is also one of the key enablers for Open-source Intelligence (OSINT). Tweets mining has recently been used in the context of incident response to estimate the location and damage caused by hurricanes and earthquakes. We aim to research the detection of a specific type of high-risk natural disasters frequently occurring and causing casualties in the Arabian Peninsula, namely `floods'. Researching how we could achieve accurate classification suitable for short informal (colloquial) Arabic text (usually used on Twitter), which is highly inconsistent and received very little attention in this field. First, we provide a thorough technical demonstration consisting of the following stages: data collection (Twitter REST API), labelling, text pre-processing, data division and representation, and training models. This has been deployed using `R' in our experiment. We then evaluate classifiers' performance via four experiments conducted to measure the impact of different stemming techniques on the following classifiers SVM, J48, C5.0, NNET, NB and k-NN. The dataset used consisted of 1434 tweets in total. Our findings show that Support Vector Machine (SVM) was prominent in terms of accuracy (F1=0.933). Furthermore, applying McNemar's test shows that using SVM without stemming on Colloquial Arabic is significantly better than using stemming techniques

discrimination of different serbian pronunciations from shtokavian dialect

Abstract This paper proposes a new methodology for discrimination of different pronunciations in the Shtokavian dialect of the Serbian language. At the first, the written language (Unicode text) is converted into codes according to the energy status of each character in the text-line. Such a set of codes is seen as a grayscale image. Then, the local structures of the image are explored by local binary operators. It creates a vector set which differentiates various pronunciations of the Serbian language. The experiment is performed on fifty documents given in Serbian language. A comparison performed between the proposed method and the n -gram method shows its clear advantage

The QMUL/HRBDT contribution to the NADI Arabic Dialect Identification Shared Task

We present the Arabic dialect identification system that we used for the country-level subtask of the NADI challenge. Our model consists of three components: BiLSTM-CNN, character-level TF-IDF, and topic modeling features. We represent each tweet using these features and feed them into a deep neural network. We then add an effective heuristic that improves the overall performance. We achieved an F1-Macro score of 20.77% and an accuracy of 34.32% on the test set. The model was also evaluated on the Arabic Online Commentary dataset, achieving results better than the state-of-the-art

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

Arabic Dialect Texts Classification

This study investigates how to classify Arabic dialects in text by extracting features which show the differences between dialects. There has been a lack of research about classification of Arabic dialect texts, in comparison to English and some other languages, due to the lack of Arabic dialect text corpora in comparison with what is available for dialects of English and some other languages. What is more, there is an increasing use of Arabic dialects 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 from Twitter, comments from Facebook and online newspapers from five groups of Arabic dialects: Gulf, Iraqi, Egyptian, Levantine, and North African. The research sought to: 1) create a dataset of Arabic dialect texts to use in training and testing the system of classification, 2) find appropriate features to classify Arabic dialects: lexical (word and multi-word-unit) and grammatical variation across dialects, 3) build a more sophisticated filter to extract features from Arabic-character written dialect text files. In this thesis, the first part describes the research motivation to show the reason for choosing the Arabic dialects as a research topic. The second part presents some background information about the Arabic language and its dialects, and the literature review shows previous research about this subject. The research methodology part shows the initial experiment to classify Arabic dialects. The results of this experiment showed the need to create an Arabic dialect text corpus, by exploring Twitter and online newspaper. The corpus used to train the ensemble classifier and to improve the accuracy of classification the corpus was extended by collecting tweets from Twitter based on the spatial coordinate points and comments from Facebook posts. The corpus was annotated with dialect labels and used in automatic dialect classification experiments. The last part of this thesis presents the results of classification, conclusions and future work