A BERT based Chinese Named Entity Recognition method on ASEAN News

Zhuang, Haoyu; Fu, Wang; Bo, Songlin; Huang, Yalin

doi:10.1088/1742-6596/1848/1/012101

Cited by 8 publications

(5 citation statements)

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“…The machine learning approaches, such as hidden Markov models (HMM), conditional random fields (CRF) and support vector machine (SVM), rely heavily on hand-crafted features (Wang et al, 2015;Yu et al, 2018) while the deep neural network model-based approach, such as convolution neural networks (CNN) and recurrent neural networks (RNN), basically rely on word or character embedding, and could extract features automatically (Dong et al, 2016;Habibi et al, 2017;Liu et al, 2019a, b). In addition, pre-trained Bidirectional Encoder Representations from Transformers (BERT) model has also been applied to entity recognition (Zhuang et al, 2021).…”

Section: Knowledge Elements and Extraction Processmentioning

confidence: 99%

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Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements

Zeng

Cao

Chen

et al. 2023

AJIM

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PurposeThis study analyzed the interdisciplinary characteristics of Chinese research studies in library and information science (LIS) measured by knowledge elements extracted through the Lexicon-LSTM model.Design/methodology/approachEight research themes were selected for experiment, with a large-scale (N = 11,625) dataset of research papers from the China National Knowledge Infrastructure (CNKI) database constructed. And it is complemented with multiple corpora. Knowledge elements were extracted through a Lexicon-LSTM model. A subject knowledge graph is constructed to support the searching and classification of knowledge elements. An interdisciplinary-weighted average citation index space was constructed for measuring the interdisciplinary characteristics and contributions based on knowledge elements.FindingsThe empirical research shows that the Lexicon-LSTM model has superiority in the accuracy of extracting knowledge elements. In the field of LIS, the interdisciplinary diversity indicator showed an upward trend from 2011 to 2021, while the disciplinary balance and difference indicators showed a downward trend. The knowledge elements of theory and methodology could be used to detect and measure the interdisciplinary characteristics and contributions.Originality/valueThe extraction of knowledge elements facilitates the discovery of semantic information embedded in academic papers. The knowledge elements were proved feasible for measuring the interdisciplinary characteristics and exploring the changes in the time sequence, which helps for overview the state of the arts and future development trend of the interdisciplinary of research theme in LIS.

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Section: Knowledge Elements and Extraction Processmentioning

confidence: 99%

“…, 2019a, b). In addition, pre-trained Bidirectional Encoder Representations from Transformers (BERT) model has also been applied to entity recognition (Zhuang et al. , 2021).…”

Section: Related Workmentioning

confidence: 99%

Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements

Zeng

Cao

Chen

et al. 2023

AJIM

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“…The greatest feature of sequence Figure 1 shows that the BERT model uses MLM (masked language model) and nextsentence prediction methods to capture sentiment representations at word and sentence levels, respectively [17]. The main structure of the BERT model is stacked by Transformer, and the text encoding of the Transformer is based on the attention mechanism [18]. Therefore, different weights can be assigned to each word according to the relationships evident between the words and sentences [19].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Running a Sustainable Social Media Business: The Use of Deep Learning Methods in Online-Comment Short Texts

Lin

Zhang

et al. 2023

Sustainability

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With the prevalence of the Internet in society, social media has considerably altered the ways in which consumers conduct their daily lives and has gradually become an important channel for online communication and sharing activities. At the same time, whoever can rapidly and accurately disseminate online data among different companies affects their sales and competitiveness; therefore, it is urgent to obtain consumer public opinions online via an online platform. However, problems, such as sparse features and semantic losses in short-text online reviews, exist in the industry; therefore, this article uses several deep learning techniques and related neural network models to analyze Weibo online-review short texts to perform a sentiment analysis. The results show that, compared with the vector representation generated by Word2Vec’s CBOW model, BERT’s word vectors can obtain better sentiment analysis results. Compared with CNN, BiLSTM, and BiGRU models, the improved BiGRU-Att model can effectively improve the accuracy of the sentiment analysis. Therefore, deep learning neural network systems can improve the quality of the sentiment analysis of short-text online reviews, overcome the problems of the presence of too many unfamiliar words and low feature density in short texts, and provide an efficient and convenient computational method for improving the ability to perform sentiment analysis of short-text online reviews. Enterprises can use online data to analyze and immediately grasp the intentions of existing or potential consumers towards the company or product through deep learning methods and develop new services or sales plans that are more closely related to consumers to increase competitiveness. When consumers experience the use of new services or products again, they may provide feedback online. In this situation, companies can use deep learning sentiment analysis models to perform additional analyses, forming a dynamic cycle to ensure the sustainable operation of their enterprises.

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“…The emergence of question and answer (Q&A) systems presents an opportunity to overcome this issue. Named entity recognition (NER) is now widely used in the military ( Wang et al., 2018 ; Lu et al., 2020 ; Baigang and Yi, 2023 ; Li et al., 2023 ), entertainment and culture ( Molina-Villegas et al., 2021 ; Zhuang et al., 2021 ; Fu et al., 2022 ; Huang et al., 2022 ), cybersecurity ( Georgescu et al., 2019 ; Simran et al., 2020 ; Chen et al., 2021 ; Ma et al., 2021 ), and medicine ( Ji et al., 2019 ; Li et al., 2020 ; Wang et al., 2020 ; Liu et al., 2022 ). However, the application of NER in the agricultural sector is still in the early stages of development ( Wang et al., 2022 ; Yu et al., 2022a ; Qian et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Improved XLNet modeling for Chinese named entity recognition of edible fungus

Yu,

Wang,

Xue

2024

Front. Plant Sci.

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IntroductionThe diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem. Named entity recognition (NER) provides the basis for building an intelligent Q&A system for edible fungus. In the field of edible fungus, there is a lack of a publicly available Chinese corpus suitable for use in NER, and conventional methods struggle to capture long-distance dependencies in the NER process.MethodsThis paper describes the establishment of a Chinese corpus in the field of edible fungus and introduces an NER method for edible fungus information based on XLNet and conditional random fields (CRFs). Our approach combines an iterated dilated convolutional neural network (IDCNN) with a CRF. First, leveraging the XLNet model as the foundation, an IDCNN layer is introduced. This layer addresses the limited capacity to capture features across utterances by extending the receptive field of the convolutional kernel. The output of the IDCNN layer is input to the CRF layer, which mitigates any labeling logic errors, resulting in the globally optimal labels for the NER task relating to edible fungus.ResultsExperimental results show that the precision achieved by the proposed model reaches 0.971, with a recall of 0.986 and an F1-score of 0.979.DiscussionThe proposed model outperforms existing approaches in terms of these evaluation metrics, effectively recognizing entities related to edible fungus information and offering methodological support for the construction of knowledge graphs.

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A BERT based Chinese Named Entity Recognition method on ASEAN News

Cited by 8 publications

References 1 publication

Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements

Measuring the interdisciplinary characteristics of Chinese research in library and information science based on knowledge elements

Running a Sustainable Social Media Business: The Use of Deep Learning Methods in Online-Comment Short Texts

Improved XLNet modeling for Chinese named entity recognition of edible fungus

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