A Recent Survey of Arabic Named Entity Recognition on Social Media

Ali, Brahim Ait Ben; Mihi, Soukaina; Bazi, Ismail El; Laachfoubi, Nabil

doi:10.18280/ria.340202

Cited by 15 publications

(7 citation statements)

References 38 publications

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“…Type Title [8] 2009 Report NERA: Named entity recognition for Arabic [9] 2014 Survey A survey of Arabic named entity recognition and classification [10] 2015 Report Named entity recognition for arabic social media [11] 2016 Survey Arabic named entity recognition -a survey and analysis [12] 2017 Survey A comparative review of machine learning for Arabic named entity recognition [13] 2019 Report Arabic named entity recognition using deep learning approach [14] 2019 Report Arabic named entity recognition: What works and what's next [15] 2020 Survey A recent survey of arabic named entity recognition on social media.…”

Section: Cite Yearmentioning

confidence: 99%

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A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

Qu¹,

Gu²,

Xia³

et al. 2023

Preprint

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As more and more Arabic texts emerged on the Internet, extracting important information from these Arabic texts is especially useful. As a fundamental technology, Named entity recognition (NER) serves as the core component in information extraction technology, while also playing a critical role in many other Natural Language Processing (NLP) systems, such as question answering and knowledge graph building. In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model. Specifically, we first introduce the background of Arabic NER, including the characteristics of Arabic and existing resources for Arabic NER. Then, we systematically review the development of Arabic NER methods. Traditional Arabic NER systems focus on feature engineering and designing domain-specific rules. In recent years, deep learning methods achieve significant progress by representing texts via continuous vector representations. With the growth of pre-trained language model, Arabic NER yields better performance. Finally, we conclude the method gap between Arabic NER and NER methods from other languages, which helps outline future directions for Arabic NER.

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Section: Cite Yearmentioning

confidence: 99%

“…Dandashi et al [11] and Salah et al [12] mainly explore the machine learning method for Arabic NER. More recently, Ali et al [15] studied the Arabic NER on social media in 2020 but performed experiments on English datasets. In summary, existing surveys do not completely cover modern DL-based NER systems.…”

Section: Anercorpmentioning

confidence: 99%

A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

Qu¹,

Gu²,

Xia³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Our corpus also includes MSA and dialectal text. For entity recognition, several NER approaches have been proposed for Arabic as reviewed in (Shaalan, 2014;Ali et al, 2020). NER approaches in Arabic can be grouped into rule-based, machine learning, and advanced deep learning approaches.…”

Section: Related Workmentioning

confidence: 99%

Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT

Jarrar¹,

Khalilia²,

Ghanem³

2022

Preprint

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This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person, organization, location, event and date. More importantly, the corpus is annotated with nested entities instead of the more common flat annotations. The data contains about 75K entities and 22.5% of which are nested. The inter-annotator evaluation of the corpus demonstrated a strong agreement with Cohen's Kappa of 0.979 and an F1-score of 0.976. To validate our data, we used the corpus to train a nested NER model based on multi-task learning using the pre-trained AraBERT (Arabic BERT). The model achieved an overall micro F1-score of 0.884. Our corpus, the annotation guidelines, the source code and the pre-trained model are publicly available.

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“…Currently, the majority of studies focus on information retrieval (IR), natural language processing (NLP) [4][5][6][7], and text classification [8][9][10][11], but there is only limited research on text segmentation, which can be further subdivided into word segmentation, feature segmentation, and sentence segmentation. Text segmentation is an important NLP task, which is fundamental to many text mining tasks, here ncluding text classification and text clustering.…”

Section: Hazard Text Segmentationmentioning

confidence: 99%

Natural Language Processing Using Neighbour Entropy-based Segmentation

Qiao

Yan

2022

CIT. J. comput. inf. technol

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In natural language processing (NLP) of Chinese hazard text collected in the process of hazard identification, Chinese word segmentation (CWS) is the first step to extracting meaningful information from such semi-structured Chinese texts. This paper proposes a new neighbor entropy-based segmentation (NES) model for CWS. The model considers the segmentation benefits of neighbor entropies, adopting the concept of "neighbor" in optimization research. It is defined by the benefit ratio of text segmentation, including benefits and losses of combining the segmentation unit with more information than other popular statistical models. In the experiments performed, together with the maximum-based segmentation algorithm, the NES model achieves a 99.3% precision, 98.7% recall, and 99.0% f-measure for text segmentation; these performances are higher than those of existing tools based on other seven popular statistical models. Results show that the NES model is a valid CWS, especially for text segmentation requirements necessitating longer-sized characters. The text corpus used comes from the Beijing Municipal Administration of Work Safety, which was recorded in the fourth quarter of 2018.

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A Recent Survey of Arabic Named Entity Recognition on Social Media

Cited by 15 publications

References 38 publications

A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT

Natural Language Processing Using Neighbour Entropy-based Segmentation

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