We explore the problem of predicting the publication period of text document,
such as a news article, using the text from that document. In order to do so,
we created our own extensive labeled dataset of over 350,000 news articles
published by The New York Times over six decades. We then provide an
implementation of a simple Naive Bayes baseline model, which surprisingly
achieves decent performance in terms of accuracy.Finally, for our approach, we
use a pretrained BERT model fine-tuned for the task of text classification.
This model exceeds our expectations and provides some very impressive results
in terms of accurately classifying news articles into their respective
publication decades. The results beat the performance of the few previously
tried models for this relatively unexplored task of time prediction from text.

本文旨在通过使用文本文档预测其发布期，使用了自行创建的全面标记的数据集，并实现了一个简单的朴素贝叶斯基线模型和一个经过微调的预训练 BERT 模型，而结果则超出了我们的预期，在准确预测新闻文章发布年代方面表现出色，击败了此前在此相对未开发任务中尝试过的少数模型。