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Previous work on using BiLSTM models for PoS tagging has primarily focused onsmall tagsets. We evaluate BiLSTM models for tagging Icelandic, amorphologically rich language, using a relatively large tagset. Our baselineBiLSTM model achieves higher accuracy than any previously published tagger nottaking advantage of a morphological lexicon. When we extend the model byincorporating such data, we outperform previous state-of-the-art results by asignificant margin. We also report on work in progress that attempts to addressthe problem of data sparsity inherent in morphologically detailed, fine-grainedtagsets. We experiment with training a separate model on only the lexicalcategory and using the coarse-grained output tag as an input for the mainmodel. This method further increases the accuracy and reduces the taggingerrors by 21.3% compared to previous state-of-the-art results. Finally, wetrain and test our tagger on a new gold standard for Icelandic.

Augmenting a BiLSTM tagger with a Morphological Lexicon and a Lexical Category Identification Step