用五类语言特征构建可解释的新闻分类模型，效果超越传统方法
LinguaSynth: Heterogeneous Linguistic Signals for News Classification
- 融合词汇、句法、实体等五类语言特征，用逻辑回归实现透明分类
- 在20 Newsgroups数据集上达84.89%准确率，比TF-IDF基线高3.32个百分点
- 适合需要可解释性与低资源消耗的文本分类场景
深度学习虽推动了自然语言处理发展，但对大型黑箱模型的依赖带来了可解释性和计算效率问题。本文提出LinguaSynth，一种新型文本分类框架，将词汇、句法、实体级、词级语义和文档级语义五类互补语言特征整合进透明的逻辑回归模型中。与基于Transformer的架构不同，LinguaSynth保持可解释性与高效计算，在20 Newsgroups数据集上取得84.89%的准确率，较稳健的TF-IDF基线提升3.32个百分点。通过严谨的特征交互分析，发现句法与实体级信号对消歧至关重要，并有效补充分布语义。LinguaSynth为可解释、低资源的NLP模型树立新基准，挑战了深度神经网络是高性能文本分类必要条件的普遍假设。
原文摘要 · Abstract (English)
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.
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