Capsule Network Algorithm for Performance Optimization of Text Classification

IF 1 4区心理学 Q3 PSYCHOLOGY, CLINICAL

Journal of Social and Clinical Psychology Pub Date : 2021-04-03 DOI:10.36548/JSCP.2021.1.001

J SamuelManoharan

引用次数: 46

Abstract

In regions of visual inference, optimized performance is demonstrated by capsule networks on structured data. Classification of hierarchical multi-label text is performed with a simple capsule network algorithm in this paper. It is further compared to support vector machine (SVM), Long Short Term Memory (LSTM), artificial neural network (ANN), convolutional Neural Network (CNN) and other neural and non-neural network architectures to demonstrate its superior performance. The Blurb Genre Collection (BGC) and Web of Science (WOS) datasets are used for experimental purpose. The encoded latent data is combined with the algorithm while handling structurally diverse categories and rare events in hierarchical multi-label text applications.

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用于文本分类性能优化的胶囊网络算法

在视觉推理领域，胶囊网络在结构化数据上展示了优化的性能。本文采用一种简单的胶囊网络算法对分层多标签文本进行分类。进一步将其与支持向量机（SVM）、长短期记忆（LSTM）、人工神经网络（ANN）、卷积神经网络（CNN）等神经网络和非神经网络架构进行比较，以证明其优越的性能。Blurb Genre Collection（BGC）和Web of Science（WOS）数据集用于实验目的。编码的潜在数据与算法相结合，同时在分层多标签文本应用程序中处理结构多样的类别和罕见事件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Social and Clinical Psychology Multiple-

CiteScore

2.90

自引率

0.00%

发文量

期刊介绍： This journal is devoted to the application of theory and research from social psychology toward the better understanding of human adaptation and adjustment, including both the alleviation of psychological problems and distress (e.g., psychopathology) and the enhancement of psychological well-being among the psychologically healthy. Topics of interest include (but are not limited to) traditionally defined psychopathology (e.g., depression), common emotional and behavioral problems in living (e.g., conflicts in close relationships), the enhancement of subjective well-being, and the processes of psychological change in everyday life (e.g., self-regulation) and professional settings (e.g., psychotherapy and counseling). Articles reporting the results of theory-driven empirical research are given priority, but theoretical articles, review articles, clinical case studies, and essays on professional issues are also welcome. Articles describing the development of new scales (personality or otherwise) or the revision of existing scales are not appropriate for this journal.