Quality Assessment of Text Data Using C-RNN

Sharaff, Aakanksha; Khurana, Sarthak; Sahu, Thalendra

doi:10.1007/978-981-16-1781-2_19

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Sharaff Aakanksha et al [11] introduced a novel method for text data quality assessment using a Deep Learning Convolutional Recurrent Neural Network (C-RNN) model, combining CNN and RNN for SMS data quality assessment; Kumar Anuj [12] employed RNN, CNN, LSTM, and Bidirectional Encoder Representation from Transformers (BERT) models to detect hate speech and aggressive language in text data. Moreover, they explored the impact of weighted and unweighted methods on the learning model system, with experiments showing that the pre-trained BERT model outperforms other models in both unweighted and weighted classifications, yet its performance is significantly hindered in scalar instances; Lee Hyejin et al [13] used an LSTM model for text classification of Flickr data to address the issues in covering features of tourist activities, demonstrating how to identify tourism categories and analyzing the Return on Attention (ROA) preferences of tourists in detail.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Huo,

Guo,

Yang

et al. 2024

Systems

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In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics.

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Section: Introductionmentioning

confidence: 99%