Feature Extraction and Prediction of Combined Text and Survey Data using Two-Staged Modeling

A. A. Neloy, M. Turgeon
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Abstract

Deep learning (DL) based natural language processing (NLP) has recently grown as one the fastest research domain and retained remarkable improvement in many applications. Due to the significant amount of data, the adaptation of feature learning and symmetric data efficiency is a critical underlying task in such applications. However, their ability to extract features is limited due to a lack of proper model formation. Moreover, the use of these methods on smaller datasets is unexplored and underdeveloped compared to more popular research areas. This work introduces a two-stage modeling approach to combine classical statistical analysis with NLP problems in a real-world dataset. We effectively layout a combination of the classical statistical model incorporating a stacked ensemble classifier and a DL framework of convolutional neural network (CNN) and Bidirectional Recurrent Neural Networks (Bi-RNN) to structure a more decomposed architecture with lower computational complexity. Additionally, the experimental results illustrating 96.69 % training and 70.56 % testing accuracy and hypothesis testing from our DL models followed by an ablation study empirically demonstrate the validation of our proposed combined modeling technique.
基于两阶段建模的文本与调查数据组合特征提取与预测
基于深度学习(DL)的自然语言处理(NLP)是近年来发展最快的研究领域之一,在许多应用中都取得了显著的进步。由于数据量巨大,特征学习的适应和对称数据的效率是这类应用的关键底层任务。然而,由于缺乏适当的模型形成,它们提取特征的能力受到限制。此外,与更流行的研究领域相比,这些方法在较小数据集上的使用是未经探索和不发达的。这项工作介绍了一种两阶段建模方法,将经典统计分析与现实世界数据集中的NLP问题结合起来。我们有效地将经典统计模型与卷积神经网络(CNN)和双向递归神经网络(Bi-RNN)的堆叠集成分类器和深度学习框架组合在一起,以构建具有更低计算复杂度的更分解的体系结构。此外,实验结果表明,我们的深度学习模型的训练准确率为96.69%,测试准确率为70.56%,并且在消融研究之后进行了假设检验,从经验上证明了我们提出的组合建模技术的有效性。
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