Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition

Su, Jianlin; Ahmed, Murtadha,; Pan, Shengfeng; Hou, Jing; Sun, Jun; Huang, Wanwei; Wen, Bin; Liu, Yunfeng

doi:10.48550/arxiv.2208.03054

Cited by 24 publications

(40 citation statements)

References 35 publications

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“…In this article, our study examines the effectiveness of this approach on Chinese nested NER tasks. Su et al (2022) provided a span-based approach for nested NER tasks, in which the method identifies the starting and ending positions to locate entities.…”

Section: Baselinesmentioning

confidence: 99%

A Chinese nested named entity recognition approach using sequence labeling

Chen

Luo

Shen³

et al. 2023

IJWIS

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Purpose This study aims to introduce an innovative approach that uses a decoder with multiple layers to accurately identify Chinese nested entities across various nesting depths. To address potential human intervention, an advanced optimization algorithm is used to fine-tune the decoder based on the depth of nested entities present in the data set. With this approach, this study achieves remarkable performance in recognizing Chinese nested entities. Design/methodology/approach This study provides a framework for Chinese nested named entity recognition (NER) based on sequence labeling methods. Similar to existing approaches, the framework uses an advanced pre-training model as the backbone to extract semantic features from the text. Then a decoder comprising multiple conditional random field (CRF) algorithms is used to learn the associations between granularity labels. To minimize the need for manual intervention, the Jaya algorithm is used to optimize the number of CRF layers. Experimental results validate the effectiveness of the proposed approach, demonstrating its superior performance on both Chinese nested NER and flat NER tasks. Findings The experimental findings illustrate that the proposed methodology can achieve a remarkable 4.32% advancement in nested NER performance on the People’s Daily corpus compared to existing models. Originality/value This study explores a Chinese NER methodology based on the sequence labeling ideology for recognizing sophisticated Chinese nested entities with remarkable accuracy.

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Section: Baselinesmentioning

confidence: 99%

A Chinese nested named entity recognition approach using sequence labeling

Chen

Luo

Shen³

et al. 2023

IJWIS

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“…In our work, we use the Global Pointer [21] as an entity recognition module, which is a span-based entity recognition method. Span-based methods identify named entities by classifying sub-sequences of sentences.…”

Section: B Global Pointermentioning

confidence: 99%

Medical Name Entity Recognition Based on Lexical Enhancement and Global Pointer

Zhang¹,

Liang²

2023

IJACSA

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Named entity recognition (NER) in biological sources, also called medical named entity recognition (MNER), attempts to identify and categorize medical terminology in electronic records. Deep neural networks have recently demonstrated substantial effectiveness in MNER. However, Chinese MNER has issues that cannot use lexical information and involve nested entities. To address these problems, we propose a model which can handle both nested and non-nested entities. The model uses a simple lexical enhancement method for merging lexical information into each character's vector representation, and then uses the Global Pointer approach for entity recognition. Furthermore, we retrain a pre-trained model with a Chinese medical corpus to incorporate medical knowledge, resulting in F1 score of 68.13% on the nested dataset CMeEE, 95.56% on the non-nested dataset CCKS2017, 85.89% on CCKS2019, and 92.08% on CCKS2020. These data demonstrate the efficacy of our proposed model.

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“…Deep learning is the current trend of event extraction research. This section mainly introduces BERT, GlobalPointer [28] and other theories and technologies related to deep learning in the field of natural language processing.…”

Section: Deep Learning Theory and Related Technology Researchmentioning

confidence: 99%

Research on intelligent recognition method of software vulnerability severity based on event extraction

Wen

Lin

et al. 2023

Preprint

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The number of vulnerability reports is rapidly increasing, and relying on human resources for vulnerability analysis will become more difficult. In addition, the current vulnerability report research is mainly based on general vulnerability disclosure (CVE) or other foreign open source projects, and there are few researches on Chinese vulnerability reports. With the development of deep learning, the research on event extraction has also been greatly developed. In order to contribute to the vulnerability analysis of Chinese vulnerability report, this paper adopts the method of event extraction to automatically mine the vulnerability report of the National Information security Vulnerability Sharing platform (CNVD). A new vulnerability analysis method is proposed, that is, the severity level of vulnerability report is evaluated by the vulnerability description information and the event information. At the same time, a convolutional neural network method named EICNN is proposed to realize this new vulnerability analysis method by integrating event information. An application that obtains the vulnerability reporting event information and evaluates the vulnerability severity at the end.

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Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition

Cited by 24 publications

References 35 publications

A Chinese nested named entity recognition approach using sequence labeling

A Chinese nested named entity recognition approach using sequence labeling

Medical Name Entity Recognition Based on Lexical Enhancement and Global Pointer

Research on intelligent recognition method of software vulnerability severity based on event extraction

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