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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.01185 |
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| _version_ | 1866929704299134976 |
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| author | Kolpakov, Alexander Rivin, Igor |
| author_facet | Kolpakov, Alexander Rivin, Igor |
| contents | In this technical note we suggest a novel approach to discover temporal (related and unrelated to language dilation) and personality (authorship attribution) aspects in historical datasets. We exemplify our approach on the State of the Union addresses given by the past 42 US presidents: this dataset is known for its relatively small amount of data, and high variability of the size and style of texts. Nevertheless, we manage to achieve about 95\% accuracy on the authorship attribution task, and pin down the date of writing to a single presidential term. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01185 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A ripple in time: a discontinuity in American history Kolpakov, Alexander Rivin, Igor Computation and Language Artificial Intelligence Machine Learning Social and Information Networks I.2.7; I.5.4; H.3.1; H.3.3 In this technical note we suggest a novel approach to discover temporal (related and unrelated to language dilation) and personality (authorship attribution) aspects in historical datasets. We exemplify our approach on the State of the Union addresses given by the past 42 US presidents: this dataset is known for its relatively small amount of data, and high variability of the size and style of texts. Nevertheless, we manage to achieve about 95\% accuracy on the authorship attribution task, and pin down the date of writing to a single presidential term. |
| title | A ripple in time: a discontinuity in American history |
| topic | Computation and Language Artificial Intelligence Machine Learning Social and Information Networks I.2.7; I.5.4; H.3.1; H.3.3 |
| url | https://arxiv.org/abs/2312.01185 |