A Multi-Embeddings Approach Coupled with Deep Learning for Arabic Named Entity Recognition

Youssef, Abeer; Elattar, Mustafa; El-Beltagy, Samhaa R.

doi:10.1109/niles50944.2020.9257975

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…In the medical domain, [88] tiveness of model ensembles in technical fields. [89] used a multi-embeddings approach coupled with deep learning for Arabic NER, achieving an F1 score of 77.62 on the AQMAR dataset, thus setting a new performance standard for NER in Arabic. [90] utilized the transformer model XLM-Roberta for Hindi NER, achieving F1-scores of 0.96 (micro) and 0.80 (macro), further validating the effectiveness of transformers in language-specific NER tasks.…”

Section: Model Evaluation and Comparisonmentioning

confidence: 99%

Transformer-Based Named Entity Recognition in Construction Supply Chain Risk Management in Australia

Shishehgarkhaneh,

Moehler,

Fang

et al. 2024

IEEE Access

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In the Australian construction industry, effective supply chain risk management (SCRM) is critical due to its complex networks and susceptibility to various risks. This study explores the application of transformer models like BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA for Named Entity Recognition (NER) in this context. Utilizing these models, we analyzed news articles to identify and classify entities related to supply chain risks, providing insights into the vulnerabilities within this sector. Among the evaluated models, RoBERTa achieved the highest average F1 score of 0.8580, demonstrating its superior balance in precision and recall for NER in the Australian construction supply chain context. Our findings highlight the potential of NLP-driven solutions to revolutionize SCRM, particularly in geo-specific settings.

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Section: Model Evaluation and Comparisonmentioning

confidence: 99%

Transformer-Based Named Entity Recognition in Construction Supply Chain Risk Management in Australia

Shishehgarkhaneh,

Moehler,

Fang

et al. 2024

IEEE Access

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“…Reference [22] is another research that combined different types of classical and contextual embeddings: pre-trained word embeddings such as FastText and Aravec, pooled contextual embeddings, and AraBERT embeddings for processing Arabic Named Entity Recognition (NER) task on the AQMAR dataset. These embeddings are then fed into the Bi-LSTM.…”

Section: Using Arabert Model In Nlp Tasksmentioning

confidence: 99%

Embedding Extraction for Arabic Text Using the AraBERT Model

Abo-Elghit¹,

Hamza²,

Al-Zoghby³

2022

Computers, Materials &Amp; Continua

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Nowadays, we can use the multi-task learning approach to train a machine-learning algorithm to learn multiple related tasks instead of training it to solve a single task. In this work, we propose an algorithm for estimating textual similarity scores and then use these scores in multiple tasks such as text ranking, essay grading, and question answering systems. We used several vectorization schemes to represent the Arabic texts in the SemEval2017-task3-subtask-D dataset. The used schemes include lexical-based similarity features, frequency-based features, and pre-trained model-based features. Also, we used contextual-based embedding models such as Arabic Bidirectional Encoder Representations from Transformers (AraBERT). We used the AraBERT model in two different variants. First, as a feature extractor in addition to the text vectorization schemes' features. We fed those features to various regression models to make a prediction value that represents the relevancy score between Arabic text units. Second, AraBERT is adopted as a pre-trained model, and its parameters are fine-tuned to estimate the relevancy scores between Arabic textual sentences. To evaluate the research results, we conducted several experiments to compare the use of the AraBERT model in its two variants. In terms of Mean Absolute Percentage Error (MAPE), the results show minor variance between AraBERT v0.2 as a feature extractor (21.7723) and the fine-tuned AraBERT v2 (21.8211). On the other hand, AraBERT v0.2-Large as a feature extractor outperforms the finetuned AraBERT v2 model on the used data set in terms of the coefficient of determination (R 2 ) values (0.014050,−0.032861), respectively.

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Named Entity Recognition for Algerian Arabic Dialect in Social Media

Dahou

Cheragui

2023

Lecture Notes in Networks and Systems

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A Multi-Embeddings Approach Coupled with Deep Learning for Arabic Named Entity Recognition

Cited by 5 publications

References 22 publications

Transformer-Based Named Entity Recognition in Construction Supply Chain Risk Management in Australia

Transformer-Based Named Entity Recognition in Construction Supply Chain Risk Management in Australia

Embedding Extraction for Arabic Text Using the AraBERT Model

Named Entity Recognition for Algerian Arabic Dialect in Social Media

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