Improving historical spelling normalization with bi-directional LSTMs and multi-task learning
Contribution for newspaper or weekly magazine, PreprintEnglishOPEN
Bollmann, Marcel; Søgaard, Anders;
Subject: Computer Science - Computation and Language
Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data.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 a... View more
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