Improving historical spelling normalization with bi-directional LSTMs and multi-task learning
Year: 2016
Type: preprint
Abstract: Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data.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.
Source: arXiv (Cornell University)
Authors: Bollmann, Marcel, Søgaard, Anders
Institutions: Ruhr University Bochum, University of Copenhagen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W2539746012