# Authorship Attribution in Bangla literature using Character-level CNN

arXiv: https://arxiv.org/abs/2001.05316 · HTML: https://mlsanity.com/paper/2001.05316/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models](https://mlsanity.com/paper/2107.03844/) (2021)
- [Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches](https://mlsanity.com/paper/2505.15422/) (2025)
- [Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT](https://mlsanity.com/paper/2403.05519/) (2024)
- [BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning](https://mlsanity.com/paper/2608.05104/) (2026)

## Abstract (arXiv)

Characters are the smallest unit of text that can extract stylometric signals to determine the author of a text. In this paper, we investigate the effectiveness of character-level signals in Authorship Attribution of Bangla Literature and show that the results are promising but improvable. The time and memory efficiency of the proposed model is much higher than the word level counterparts but accuracy is 2-5% less than the best performing word-level models. Comparison of various word-based models is performed and shown that the proposed model performs increasingly better with larger datasets. We also analyze the effect of pre-training character embedding of diverse Bangla character set in authorship attribution. It is seen that the performance is improved by up to 10% on pre-training. We used 2 datasets from 6 to 14 authors, balancing them before training and compare the results.