Language identification (" LI") is the problem of determining the natural language that a
document or part thereof is written in. Automatic LI has been extensively researched for over …

We present the results of the VarDial Evaluation Campaign on Natural Language
Processing (NLP) for Similar Languages, Varieties and Dialects, which we organized as part …

State-of-the-art natural language processing (NLP) models are trained on massive training
corpora, and report a superlative performance on evaluation datasets. This survey delves …

The Perso-Arabic scripts are a family of scripts that are widely adopted and used by various
linguistic communities around the globe. Identifying various languages using such scripts is …

This study proposes a hybrid deep learning models called attention-based CNN-BiLSTM
(ACBiL) for dialect identification on Javanese text. Our ACBiL model comprises of input …

This paper presents a novel neural architecture capable of outperforming state-of-the-art
systems on the task of language variety classification. The architecture is a hybrid that …

The aim of this paper is to apply a corpus-based methodology, based on the measure of
perplexity, to automatically calculate the cross-lingual language distance between historical …

This work investigates the task of identifying the language of digitally encoded text.
Automatic methods for language identification have been developed since the 1960s …

Twitter is a well-known social media platform with over 500 million users worldwide. More
than 100 languages were identified from among a million tweets. However, only 34 formal …

We propose a hierarchical neural network model for language variety identification that
integrates information from a social network. Recently, language variety identification has …