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arXiv · 1610.07844

Improving historical spelling normalization with bi-directional LSTMs and multi-task learning

Marcel Bollmann·Anders Søgaard
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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Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data.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.

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Marcel Bollmann, Anders Søgaard. 2016-10-25. Improving historical spelling normalization with bi-directional LSTMs and multi-task learning. https://arxiv.org/abs/1610.07844

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