Abstract
Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling of historical words to modern forms. We explore the suitability of a deep neural network architecture for this task, particularly a deep bi-LSTM network applied on a character level. Our model compares well to previously established normalization algorithms when evaluated on a diverse set of texts from Early New High German. We show that multi-task learning with additional normalization data can improve our model’s performance further.- Anthology ID:
- C16-1013
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 131–139
- Language:
- URL:
- https://aclanthology.org/C16-1013
- DOI:
- Cite (ACL):
- Marcel Bollmann and Anders Søgaard. 2016. Improving historical spelling normalization with bi-directional LSTMs and multi-task learning. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 131–139, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Improving historical spelling normalization with bi-directional LSTMs and multi-task learning (Bollmann & Søgaard, COLING 2016)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/C16-1013.pdf