Abstract
Deep learning has significantly advanced NLP, but its reliance on large black-box models introduces critical interpretability and computational efficiency concerns. This paper proposes LinguaSynth, a novel text classification framework that strategically integrates five complementary linguistic feature types: lexical, syntactic, entity-level, word-level semantics, and document-level semantics within a transparent logistic regression model. Unlike transformer-based architectures, LinguaSynth maintains interpretability and computational efficiency, achieving an accuracy of 84.89 percent on the 20 Newsgroups dataset and surpassing a robust TF-IDF baseline by 3.32 percent. Through rigorous feature interaction analysis, we show that syntactic and entity-level signals provide essential disambiguation and effectively complement distributional semantics. LinguaSynth sets a new benchmark for interpretable, resource-efficient NLP models and challenges the prevailing assumption that deep neural networks are necessary for high-performing text classification.
Abstract (translated)
深度学习在自然语言处理（NLP）领域取得了显著进展，但其对大型黑箱模型的依赖引发了关键的可解释性和计算效率问题。本文提出了LinguaSynth，这是一种新颖的文本分类框架，它策略性地整合了五种互补的语言特征类型：词汇、句法、实体级、词级别语义和文档级别语义，在一个透明的逻辑回归模型中使用这些特征。与基于变换器（transformer）的架构不同，LinguaSynth保持了可解释性和计算效率，在20 Newsgroups数据集上实现了84.89% 的准确率，并比坚固的TF-IDF基线高出3.32个百分点。通过严格的特征交互分析，我们证明句法和实体级信号提供了重要的消歧作用，并且有效地补充了分布语义学。LinguaSynth为可解释、资源高效的NLP模型设立了新的基准，并挑战了深度神经网络对于高性能文本分类是必要的这一普遍假设。
URL
https://arxiv.org/abs/2506.21848
