用可解释的机器学习模型，精准判断英文历史文本的年代，还能看清语言演变规律。
Decoding the Past: Explainable Machine Learning Models for Dating Historical Texts
- 融合五类特征，构建可解释的树模型预测文本年代。
- 百年级预测准确率76.7%，十年级达26.1%，远超随机基准。
- 模型可解释性强，揭示19世纪是语言演变的关键转折点。
准确判定历史文本年代对文化遗产整理与解读至关重要。本文采用可解释的、基于特征工程的树模型进行时间分类，整合压缩、词汇结构、可读性、新词检测和距离特征五类信息，预测跨越五个世纪的英文文本年代。对比分析表明，这些特征提供互补的时间信号，综合模型性能优于任一单一特征集。在大规模语料上，百年级预测准确率达76.7%，十年级为26.1%，显著高于随机基线（20% 和 2.3%）。在放宽精度要求下，百年级前2名准确率达96.0%，十年级前10名达85.8%。模型具备强排序能力，AUCROC最高达94.8%，AUPRC最高达83.3%，平均绝对误差分别为27年和30年。针对认证任务，在关键阈值（如1850–1900）附近，二分类模型准确率可达85%–98%。特征重要性分析显示，距离特征和词汇结构最有效，压缩特征提供补充信号。SHAP解释揭示系统性语言演化模式，19世纪成为多特征域的转折点。在Project Gutenberg数据集上的跨域评估显示准确率下降26.4个百分点，但树模型仍以高效与可解释性，成为神经网络的可扩展替代方案。
原文摘要 · Abstract (English)
Accurately dating historical texts is essential for organizing and interpreting cultural heritage collections. This article addresses temporal text classification using interpretable, feature-engineered tree-based machine learning models. We integrate five feature categories - compression-based, lexical structure, readability, neologism detection, and distance features - to predict the temporal origin of English texts spanning five centuries. Comparative analysis shows that these feature domains provide complementary temporal signals, with combined models outperforming any individual feature set. On a large-scale corpus, we achieve 76.7% accuracy for century-scale prediction and 26.1% for decade-scale classification, substantially above random baselines (20% and 2.3%). Under relaxed temporal precision, performance increases to 96.0% top-2 accuracy for centuries and 85.8% top-10 accuracy for decades. The final model exhibits strong ranking capabilities with AUCROC up to 94.8% and AUPRC up to 83.3%, and maintains controlled errors with mean absolute deviations of 27 years and 30 years, respectively. For authentication-style tasks, binary models around key thresholds (e.g., 1850-1900) reach 85-98% accuracy. Feature importance analysis identifies distance features and lexical structure as most informative, with compression-based features providing complementary signals. SHAP explainability reveals systematic linguistic evolution patterns, with the 19th century emerging as a pivot point across feature domains. Cross-dataset evaluation on Project Gutenberg highlights domain adaptation challenges, with accuracy dropping by 26.4 percentage points, yet the computational efficiency and interpretability of tree-based models still offer a scalable, explainable alternative to neural architectures.
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