Named Entity Recognition using CRF with Active Learning Algorithm in English Texts

VeeraSekharReddy, B.; Rao, Koppula Srinivas; Koppula, Neeraja

doi:10.1109/iceca55336.2022.10009592

Cited by 17 publications

(1 citation statement)

References 14 publications

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“…Considering the significant reliance on labor-intensive and time-consuming manual operations for entity recognition in the agricultural domain, this study aims to overcome this bottleneck by constructing a high-quality corpus specifically designed for agricultural named entity recognition, providing a robust foundation for information extraction within this domain [28]. Building upon this, we introduce the innovative BCA-BILSTM-CRF model, which integrates the deep contextual understanding capabilities of BERTwwm, the long-distance dependency capturing advantages of BILSTM, and the excellent performance of CRF in sequence labeling [29]. Furthermore, the dynamic feature tuning provided by the channel attention (CA) mechanism effectively enhances the accuracy of entity recognition in complex agricultural texts [30].…”

Section: Introductionmentioning

confidence: 99%

Based on BERT-wwm for Agricultural Named Entity Recognition

Huang,

Tao,

et al. 2024

Agronomy

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With the continuous advancement of information technology in the agricultural field, a large amount of unstructured agricultural textual information has been generated. This information is crucial for supporting the development of smart agriculture, making the application of named entity recognition in the agricultural field more urgent. In order to enhance the accuracy of agricultural entity recognition, this study utilizes the pre-trained BERT-wwm model for word embedding into the text. Additionally, a channel attention mechanism (CA) is introduced in the BILSTM-CRF downstream feature extraction network to comprehensively capture the contextual features of the text. Experimental results demonstrate that the proposed method significantly improves the performance of named entity recognition, with increased accuracy, recall, and F1 value. The successful implementation of this method provides reliable support for downstream tasks such as agricultural knowledge graph construction and question and answer systems and establishes a foundation for better understanding and utilization of agricultural textual information.

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Section: Introductionmentioning

confidence: 99%

Based on BERT-wwm for Agricultural Named Entity Recognition

Huang,

Tao,

et al. 2024

Agronomy

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Falcon 7b for Software Mention Detection in Scholarly Documents

Khan,

Ramadan,

Yang

et al. 2024

Lecture Notes in Computer Science

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This paper aims to tackle the challenge posed by the increasing integration of software tools in research across various disciplines by investigating the application of Falcon-7b for the detection and classification of software mentions within scholarly texts. Specifically, the study focuses on solving Subtask I of the Software Mention Detection in Scholarly Publications (SOMD), which entails identifying and categorizing software mentions from academic literature. Through comprehensive experimentation, the paper explores different training strategies, including a dual-classifier approach, adaptive sampling, and weighted loss scaling, to enhance detection accuracy while overcoming the complexities of class imbalance and the nuanced syntax of scholarly writing. The findings highlight the benefits of selective labelling and adaptive sampling in improving the model’s performance. However, they also indicate that integrating multiple strategies does not necessarily result in cumulative improvements. This research offers insights into the effective application of large language models for specific tasks such as SOMD, underlining the importance of tailored approaches to address the unique challenges presented by academic text analysis.

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