Stylistic vocabulary based document dating: Frequency-based vs. RoBERTa models
(2026) STAN40 20261Department of Statistics
- Abstract
- The thesis explores historical document dating based solely on vocabulary. That is, we do not consider contextual clues such as years or mentions of historical events in the classification. Both a probability model based on previous research as well as a modern deep learning model using RoBERTa are considered. The thesis demonstrates the difficulty of striking an equilibrium between complexity and precision when working with an unbalanced dataset. Despite its more powerful nature, we
find that RoBERTa does not outperform a simpler frequency-based model in classification of fictional short stories into their publication time intervals.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/student-papers/record/9234335
- author
- Kuresevic, Ida Zoe LU
- supervisor
- organization
- course
- STAN40 20261
- year
- 2026
- type
- H1 - Master's Degree (One Year)
- subject
- language
- English
- id
- 9234335
- date added to LUP
- 2026-06-16 10:16:18
- date last changed
- 2026-06-16 10:16:18
@misc{9234335,
abstract = {{The thesis explores historical document dating based solely on vocabulary. That is, we do not consider contextual clues such as years or mentions of historical events in the classification. Both a probability model based on previous research as well as a modern deep learning model using RoBERTa are considered. The thesis demonstrates the difficulty of striking an equilibrium between complexity and precision when working with an unbalanced dataset. Despite its more powerful nature, we
find that RoBERTa does not outperform a simpler frequency-based model in classification of fictional short stories into their publication time intervals.}},
author = {{Kuresevic, Ida Zoe}},
language = {{eng}},
note = {{Student Paper}},
title = {{Stylistic vocabulary based document dating: Frequency-based vs. RoBERTa models}},
year = {{2026}},
}