A Deep Learning Architecture for Psychometric Natural Language Processing
A Deep Learning Architecture for Psychometric Natural Language Processing
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DOI：
10.1145/3365211
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发表时间：
2020-02
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通讯作者：
Faizan Ahmad;A. Abbasi;Jingjing Li;David G. Dobolyi;Richard G. Netemeyer;G. Clifford;Hsinchun Chen
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文献类型：
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作者：
Faizan Ahmad;A. Abbasi;Jingjing Li;David G. Dobolyi;Richard G. Netemeyer;G. Clifford;Hsinchun Chen
反映人们的知识、能力、态度和性格特征的心理测量对于许多现实世界的应用至关重要，例如电子商务、医疗保健和网络安全。然而，传统方法无法及时、不引人注目地收集和测量丰富的心理测量维度。因此，尽管心理测量维度很重要，但自然语言处理和信息检索界对心理测量维度的关注有限。在本文中，我们提出了一种深度学习架构 PyNDA，用于从用户生成的文本中提取心理测量维度。 PyNDA 包含新颖的表示嵌入、人口统计嵌入、结构方程模型 (SEM) 编码器和多任务学习机制，旨在协同工作，以解决与提取丰富、复杂和以用户为中心的心理测量维度相关的独特挑战。我们对包含 11 个心理测量维度（包括信任、焦虑和读写能力）的三个现实世界数据集进行的实验表明，PyNDA 明显优于传统的基于特征的分类器以及最先进的深度学习架构。消融分析表明，PyNDA 的每个组件对其整体性能都有显着贡献。总的来说，结果证明了所提出的架构对于促进丰富的心理测量分析的有效性。我们的结果对于以用户为中心的信息提取和检索系统具有重要意义，该系统旨在测量和合并心理测量维度。
Psychometric measures reflecting people’s knowledge, ability, attitudes, and personality traits are critical for many real-world applications, such as e-commerce, health care, and cybersecurity. However, traditional methods cannot collect and measure rich psychometric dimensions in a timely and unobtrusive manner. Consequently, despite their importance, psychometric dimensions have received limited attention from the natural language processing and information retrieval communities. In this article, we propose a deep learning architecture, PyNDA, to extract psychometric dimensions from user-generated texts. PyNDA contains a novel representation embedding, a demographic embedding, a structural equation model (SEM) encoder, and a multitask learning mechanism designed to work in unison to address the unique challenges associated with extracting rich, sophisticated, and user-centric psychometric dimensions. Our experiments on three real-world datasets encompassing 11 psychometric dimensions, including trust, anxiety, and literacy, show that PyNDA markedly outperforms traditional feature-based classifiers as well as the state-of-the-art deep learning architectures. Ablation analysis reveals that each component of PyNDA significantly contributes to its overall performance. Collectively, the results demonstrate the efficacy of the proposed architecture for facilitating rich psychometric analysis. Our results have important implications for user-centric information extraction and retrieval systems looking to measure and incorporate psychometric dimensions.