ABioNER: A BERT‐Based Model for Arabic Biomedical Named‐Entity Recognition

Boudjellal, Nada; Zhang, Huaping; Khan, Asif; Ahmad, Arshad; Naseem, Rashid; Shang, Jianyun; Dai, Lin

doi:10.1155/2021/6633213

Cited by 40 publications

(20 citation statements)

References 16 publications

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“…In a related study, Boudjellal [22] presented a BERTbased model to identify biomedical named entities in Arabic text data that investigates the effectiveness of pertaining a monolingual Bidirectional Encoder Representations from Transformers (BERT) model with a small-scale biomedical dataset on enhancing the model understanding of Arabic biomedical text. When the model's performance was compared to that of two state-of-the-art models, it outperformed both with an F1 score of 85%.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

Salah

Mukred

Zakaria

et al. 2022

Journal of Mathematics

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Named entity recognition (NER) is fundamental in several natural language processing applications. It involves finding and categorizing text into predefined categories such as a person's name, location, and so on. One of the most famous approaches to identify named entity is the rule-based approach. This paper introduces a rule-based NER method that can be used to examine Classical Arabic documents. The proposed method relied on triggers words, patterns, gazetteers, rules, and blacklists generated by the linguistic information about entities named in Arabic. The method operates in three stages, operational stage, preprocessing stage, and processing the rule application stage. The proposed approach was evaluated, and the results indicate that this approach achieved a 90.2% rate of precision, an 89.3% level of recall, and an F-measure of 89.5%. This new approach was introduced to overcome the challenges related to coverage in rule-based NER systems, especially when dealing with Classical Arabic texts. It improved their performance and allowed for automated rule updates. The grammar rules, gazetteers, blacklist, patterns, and trigger words were all integrated into the rule-based system in this way.

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Section: Related Workmentioning

confidence: 99%

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

Salah

Mukred

Zakaria

et al. 2022

Journal of Mathematics

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“…The CNN model outperformed other models and achieved an accuracy of 81%. Moreover, recently suggested BERT-based Arabic named entity recognition models [67,68] could also be applied for HS recognition if trained on offensive and derogatory terms. Unlike the previous works, in this paper we propose a model trained on many DA offensive and hate speech datasets as well as MSA and evaluate a new pre-trained model to classify DA.…”

Section: Hate and Offensive Speech Detection In Arabicmentioning

confidence: 99%

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

et al. 2021

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As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or communities only, extending beyond these areas into people’s everyday lives. This study investigates offensive and hate speech on Arab social media to build an accurate offensive and hate speech detection system. More precisely, we develop a classification system for determining offensive and hate speech using a multi-task learning (MTL) model built on top of a pre-trained Arabic language model. We train the MTL model on the same task using cross-corpora representing a variation in the offensive and hate context to learn global and dataset-specific contextual representations. The developed MTL model showed a significant performance and outperformed existing models in the literature on three out of four datasets for Arabic offensive and hate speech detection tasks.

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“…ProteinLM [269] was recently proposed, which trained a large-scale pre-train model for evolutionary-scale protein sequences, and the trained model is available at 23 . More recently, DeepMind develops Alphafold2 [97] that could predict protein structures with a high accuracy, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14).…”

Section: Language Models For Biological Sequencesmentioning

confidence: 99%

“…Instead of treating the BioNER as the sequence labeling problem, Sun et al [229] proposed to consider the BioNER as the machine reading comprehension (MRC) problem based on BERT. Besides English, there is much work exploring the pre-trained language models on the BioNER of other languages, including Chinese [43,91,129,130,252,272], Spanish [6,75,159], French [41], Korean [109], Russian [154], Arabic [23], Italian [28]. In Table 6, we summary the commonly used datasets in the BioNER task.…”

Section: Named Entity Recognition Biomedical Named Entity Recognition...mentioning

confidence: 99%

Pre-trained Language Models in Biomedical Domain: A Systematic Survey

Wang¹,

Xie²,

Pei³

et al. 2021

Preprint

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Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks. This also benefits biomedical domain: researchers from informatics, medicine, and computer science (CS) communities proposes various PLMs trained on biomedical datasets, e.g., biomedical text, electronic health records, protein, and DNA sequences for various biomedical tasks. However, the cross-discipline characteristics of biomedical PLMs hinder their spreading among communities; some existing works are isolated from each other without comprehensive comparison and discussions. It expects a survey that not only systematically reviews recent advances of biomedical PLMs and their applications but also standardizes terminology and benchmarks. In this paper, we summarize the recent progress of pre-trained language models in the biomedical domain and their applications in biomedical downstream tasks. Particularly, we discuss the motivations and propose a taxonomy of existing biomedical PLMs. Their applications in biomedical downstream tasks are exhaustively discussed. At last, we illustrate various limitations and future trends, which we hope can provide inspiration for the future research of the research community.CCS Concepts: • Computing methodologies → Natural language processing; Natural language generation; Neural networks; Bio-inspired approaches.

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ABioNER: A BERT‐Based Model for Arabic Biomedical Named‐Entity Recognition

Cited by 40 publications

References 16 publications

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

Pre-trained Language Models in Biomedical Domain: A Systematic Survey

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