Nested Named Entity Recognition with Span-level Graphs

Wan, Juncheng; Ru, Dongyu; Zhang, Weinan; Yan, Yu

doi:10.18653/v1/2022.acl-long.63

Cited by 39 publications

(17 citation statements)

References 25 publications

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“…In this way, they can quickly adapt to fine-grained entities. [114] or model the nested ner as latent lexicalized constituency parsing [115]. With these methods, we can solve Arabic nested NER in a more comfortable way.…”

Section: Future Directionmentioning

confidence: 99%

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: Future Directionmentioning

confidence: 99%

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

Qu¹,

Gu²,

Xia³

et al. 2023

Preprint

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show abstract

“…Many methods are based on span-level labeling and word representation. wan [9] studied span-level graphs to solve the nested named entity recognition (NER) problem. Wan [10] and wei et al used Span-based Multi-Modal Attention Network to complete the task of information extraction.…”

Section: Span-level Approachesmentioning

confidence: 99%

Joint entity and relation extraction with part-of-speech-aware attention and dependency parsing embedding

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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Joint entity and relation extraction is an important task in natural language processing, whose purpose is to obtain all triples in text. However, the existing models seldom pay attention to the part-of-speech(pos) of each word and the dependency parsing(dp) in the sentence. To solve these problems. a joint extraction model with part-of-speech-aware attention and dependency parsing embedding is proposed, named PADPE. The proposed model obtains better word representation through pos-aware attention mechanism. In addition, the parts of speech and dependency characteristics are integrated respectively in entity classification and relation classification to improve the accuracy of the classifier. The experimental results demonstrate that our model can solve the overlapping triple problem more effectively and outperform other baselines on three public datasets.

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“…• Span-level Graphs [37] use retrieval-based span-level graphs, connecting spans and entities in the training data based on n-gram features to improve the span representation.…”

Section: A Baselines On Nested Nermentioning

confidence: 99%

GFNER: A Unified Global Feature-Aware Framework for Flat and Nested Named Entity Recognition

Chen,

Pan

et al. 2023

IEEE Access

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Named Entity Recognition (NER) poses challenges for both flat and nested tasks, which require different paradigms. To overcome this issue, we present GFNER, a unified global feature-aware framework based on table filling, that can handle both types of tasks with low computational cost. While pretrained models have shown great promise in NER, they typically focus on local contextual information, disregarding global relationships that are crucial for accurate entity boundary extraction. To address this limitation, we introduce a global feature learning module that captures the inter-entity associations and significantly enhances entity recognition. Experimental results on flat and nested NER datasets demonstrate that GFNER outperforms previous state-of-the-art models. The code for GFNER is available at https://github.com/cjymz886/GFNER. INDEX TERMSNested named entity recognition, unified framework, table filling module, global feature learning module.

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Nested Named Entity Recognition with Span-level Graphs

Cited by 39 publications

References 25 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

Joint entity and relation extraction with part-of-speech-aware attention and dependency parsing embedding

GFNER: A Unified Global Feature-Aware Framework for Flat and Nested Named Entity Recognition

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