Named Entity Recognition in Equipment Support Field Using Tri-Training Algorithm and Text Information Extraction Technology

Liu, Chenguang; Yu, Yongli; Li, Xingxin; Wang, Peng

doi:10.1109/access.2021.3109911

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…Over the years, various approaches have been proposed for NER, including rule-based methods, statistical methods, and deep learning methods. In recent years, deep learning methods, particularly those based on Transformer models, have shown significant promise for NER use cases [7,8,9].…”

Section: Literature Reviewmentioning

confidence: 99%

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

2024

Journal of Electrical Systems

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Named Entity Recognition (NER) is a critical task in natural language processing that involves identifying and categorizing named entities in text. These entities can include people, organizations, locations, and more. Extracting features from these entities is equally important as it enables downstream tasks like sentiment analysis, text classification, and more. In this research paper, we propose an efficient Transformer-based model for identifying named entities and analyzing their features through ensemble operations. Our proposed model leverages the power of Transformer models such as BERT and XLNet for identifying named entities. We then convert the identified entities into feature vector sets using a combination of BERT and XLNet. These features are classified using the GoogLeNet convolutional neural network for model validation operations. By combining these different models through ensemble operations, we aim to improve the accuracy, precision, recall, and delay of the model for different use cases. The need for such a model arises due to the limitations of existing models for named entity recognition and feature analysis. While these models have achieved significant success, they still suffer from low accuracy, precision, recall, and high delay. Our proposed model overcomes these limitations by using ensemble operations to combine the strengths of different models. We compare the performance of our proposed model with existing models on standard datasets and show that it outperforms these models in terms of accuracy, precision, recall, and delay. Our results demonstrate the potential of ensemble operations in developing efficient models for named entity recognition and feature analysis. Overall, this research paper contributes to the development of more accurate and efficient models for natural language processing tasks.

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Section: Literature Reviewmentioning

confidence: 99%

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

2024

Journal of Electrical Systems

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“…This method can handle high-dimensional data with excellent accuracy. Deep learning is utilized the most frequently in Korean [34,35], Polish [36], Chinese [37], Arabic [38], and so on. The F1 value of the NER algorithm, the BI-LSTM-CRF model, on weaponry equipment names in Chinese reached 93.88% [37].…”

Section: Named Entity Recognition Techniquesmentioning

confidence: 99%

“…Deep learning is utilized the most frequently in Korean [34,35], Polish [36], Chinese [37], Arabic [38], and so on. The F1 value of the NER algorithm, the BI-LSTM-CRF model, on weaponry equipment names in Chinese reached 93.88% [37]. Based on the review of NER methods, the approach of stacking deep learning models was reliable in terms of accuracy and capability for processing complicated data.…”

Section: Named Entity Recognition Techniquesmentioning

confidence: 99%

Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

An,

Pan,

Liu

et al. 2023

Applied Sciences

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Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly.

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“…It has been widely applied to support many downstream tasks, including entity linking [2], relational triple extraction [3], sentiment analysis [4], etc. In contrast to the general NER, vertical domains such as finance, medicine, and the chemical industry require customized solutions based on their particular industry peculiarities [5]. As a semantic analysis technology, NER is a critical tool for extracting vital information from unstructured text in these vertical fields.…”

Section: Introductionmentioning

confidence: 99%

Domain-Specific Entity Recognition as Token-Pair Relation Classification

Liu,

Shi,

et al. 2023

IEEE Access

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Named Entity Recognition (NER) is a fundamental but crucial task in natural language understanding, aiming at identifying entity mentions from free text. Current methods mainly use sequencelabeling and span-based models, where the former ignores the importance of token interaction, and the latter pays little attention to the global inter-dependency among entity tokens. In this work, we propose a novel NER model that consists of two branches: a Token-Pair Interaction Module (TPIM) and a U-shaped Network. The TPIM models head-tail relations between token pairs while capturing intrinsic token connectivity within entity boundaries. The U-shaped Network is employed to capture the contextual dependency in the token-pair relation matrix. Furthermore, we build a typical domain-specific entity dataset CCAEE based on real-world applications in the chemical accident domain. The experimental results on CCAEE and CLUENER datasets demonstrate that the proposed model can achieve competitive performance compared with popular NER methods.

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Named Entity Recognition in Equipment Support Field Using Tri-Training Algorithm and Text Information Extraction Technology

Cited by 11 publications

References 32 publications

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

Domain-Specific Entity Recognition as Token-Pair Relation Classification

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