Abstract
Previous work on using BiLSTM models for PoS tagging has primarily focused on small tagsets. We evaluate BiLSTM models for tagging Icelandic, a morphologically rich language, using a relatively large tagset. Our baseline BiLSTM model achieves higher accuracy than any other previously published tagger, when not taking advantage of a morphological lexicon. When we extend the model by incorporating such data, we outperform the earlier state-of-the-art results by a significant margin. We also report on work in progress that attempts to address the problem of data sparsity inherent to morphologically detailed, fine-grained tagsets. We experiment with training a separate model on only the lexical category and using the coarse-grained output tag as an input into to the main model. This method further increases the accuracy and reduces the tagging errors by 21.3% compared to previous state-of-the-art results. Finally, we train and test our tagger on a new gold standard for Icelandic.- Anthology ID:
- R19-1133
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
- Month:
- September
- Year:
- 2019
- Address:
- Varna, Bulgaria
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 1161–1168
- Language:
- URL:
- https://aclanthology.org/R19-1133
- DOI:
- 10.26615/978-954-452-056-4_133
- Cite (ACL):
- Steinþór Steingrímsson, Örvar Kárason, and Hrafn Loftsson. 2019. Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1161–1168, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step (Steingrímsson et al., RANLP 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/R19-1133.pdf
- Code
- steinst/ABLTagger