A ripple in time: a discontinuity in American history

• URL: http://arxiv.org/abs/2312.01185v4
• Date: Sat, 4 May 2024 09:15:43 GMT
• Title: A ripple in time: a discontinuity in American history
• Authors: Alexander Kolpakov, Igor Rivin,
• Abstract summary: In this note we use the State of the Union Address dataset from Kaggle to make some surprising observations. Our main approach is using vector embeddings, such as BERT (DistilBERT) and GPT-2. In our case, no model fine-tuning is required, and the pre-trained out-of-the-box GPT-2 model is enough.
• Score: 49.84018914962972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this note we use the State of the Union Address (SOTU) dataset from Kaggle to make some surprising (and some not so surprising) observations pertaining to the general timeline of American history, and the character and nature of the addresses themselves. Our main approach is using vector embeddings, such as BERT (DistilBERT) and GPT-2. While it is widely believed that BERT (and its variations) is most suitable for NLP classification tasks, we find out that GPT-2 in conjunction with nonlinear dimension reduction methods such as UMAP provide better separation and stronger clustering. This makes GPT-2 + UMAP an interesting alternative. In our case, no model fine-tuning is required, and the pre-trained out-of-the-box GPT-2 model is enough. We also used a fine-tuned DistilBERT model for classification detecting which President delivered which address, with very good results (accuracy 93% - 95% depending on the run). An analogous task was performed to determine the year of writing, and we were able to pin it down to about 4 years (which is a single presidential term). It is worth noting that SOTU addresses provide relatively small writing samples (with about 8'000 words on average, and varying widely from under 2'000 words to more than 20'000), and that the number of authors is relatively large (we used SOTU addresses of 42 US presidents). This shows that the techniques employed turn out to be rather efficient, while all the computations described in this note can be performed using a single GPU instance of Google Colab. The accompanying code is available on GitHub.

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