NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task

Abdul-Mageed, Muhammad; Elmadany, AbdelRahim; Zhang, Chiyu; Nagoudi, El Moatez Billah; Bouamor, Houda; Habash, Nizar

doi:10.18653/v1/2023.arabicnlp-1.62

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…This presents a unique challenge for the Arabic language. Our objective is to identify transformation-based methods that yield better and more profound results than current approaches for analyzing and understanding sentiments in Arabic (Abdul-Mageed et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

Alosaimi,

Saleh,

Hamzah

et al. 2024

Front. Artif. Intell.

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Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

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Section: Introductionmentioning

confidence: 99%

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

Alosaimi,

Saleh,

Hamzah

et al. 2024

Front. Artif. Intell.

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Sequence-to-Sequence Models and Their Evaluation for Spoken Language Normalization of Slovenian

Sepesy Maučec,

Verdonik,

Donaj

2024

Applied Sciences

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Sequence-to-sequence models have been applied to many challenging problems, including those in text and speech technologies. Normalization is one of them. It refers to transforming non-standard language forms into their standard counterparts. Non-standard language forms come from different written and spoken sources. This paper deals with one such source, namely speech from the less-resourced highly inflected Slovenian language. The paper explores speech corpora recently collected in public and private environments. We analyze the efficiencies of three sequence-to-sequence models for automatic normalization from literal transcriptions to standard forms. Experiments were performed using words, subwords, and characters as basic units for normalization. In the article, we demonstrate that the superiority of the approach is linked to the choice of the basic modeling unit. Statistical models prefer words, while neural network-based models prefer characters. The experimental results show that the best results are obtained with neural architectures based on characters. Long short-term memory and transformer architectures gave comparable results. We also present a novel analysis tool, which we use for in-depth error analysis of results obtained by character-based models. This analysis showed that systems with similar overall results can differ in the performance for different types of errors. Errors obtained with the transformer architecture are easier to correct in the post-editing process. This is an important insight, as creating speech corpora is a time-consuming and costly process. The analysis tool also incorporates two statistical significance tests: approximate randomization and bootstrap resampling. Both statistical tests confirm the improved results of neural network-based models compared to statistical ones.

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Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism

Yafooz

2024

Information

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Recently, the widespread use of social media and easy access to the Internet have brought about a significant transformation in the type of textual data available on the Web. This change is particularly evident in Arabic language usage, as the growing number of users from diverse domains has led to a considerable influx of Arabic text in various dialects, each characterized by differences in morphology, syntax, vocabulary, and pronunciation. Consequently, researchers in language recognition and natural language processing have become increasingly interested in identifying Arabic dialects. Numerous methods have been proposed to recognize this informal data, owing to its crucial implications for several applications, such as sentiment analysis, topic modeling, text summarization, and machine translation. However, Arabic dialect identification is a significant challenge due to the vast diversity of the Arabic language in its dialects. This study introduces a novel hybrid machine and deep learning model, incorporating an attention mechanism for detecting and classifying Arabic dialects. Several experiments were conducted using a novel dataset that collected information from user-generated comments from Twitter of Arabic dialects, namely, Egyptian, Gulf, Jordanian, and Yemeni, to evaluate the effectiveness of the proposed model. The dataset comprises 34,905 rows extracted from Twitter, representing an unbalanced data distribution. The data annotation was performed by native speakers proficient in each dialect. The results demonstrate that the proposed model outperforms the performance of long short-term memory, bidirectional long short-term memory, and logistic regression models in dialect classification using different word representations as follows: term frequency-inverse document frequency, Word2Vec, and global vector for word representation.

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NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task

Cited by 3 publications

References 29 publications

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory

Sequence-to-Sequence Models and Their Evaluation for Spoken Language Normalization of Slovenian

Enhancing Arabic Dialect Detection on Social Media: A Hybrid Model with an Attention Mechanism

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