Towards Never Ending Language Learning for Morphologically Rich Languages
Kseniya Buraya, Lidia Pivovarova, Sergey Budkov, Andrey Filchenkov
Abstract
This work deals with ontology learning from unstructured Russian text. We implement one of components Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologicaly rich free-word-order language. We demonstrate that this method may be successfully applied to Russian data. In addition we perform several additional experiments comparing different settings of the training process. We demonstrate that utilizing of morphological features significantly improves the system precision while using of seed patterns helps to improve the coverage.- Anthology ID:
- W17-1417
- Volume:
- Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Tomaž Erjavec, Jakub Piskorski, Lidia Pivovarova, Jan Šnajder, Josef Steinberger, Roman Yangarber
- Venue:
- BSNLP
- SIG:
- SIGSLAV
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 108–118
- Language:
- URL:
- https://aclanthology.org/W17-1417
- DOI:
- 10.18653/v1/W17-1417
- Cite (ACL):
- Kseniya Buraya, Lidia Pivovarova, Sergey Budkov, and Andrey Filchenkov. 2017. Towards Never Ending Language Learning for Morphologically Rich Languages. In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing, pages 108–118, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Towards Never Ending Language Learning for Morphologically Rich Languages (Buraya et al., BSNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/W17-1417.pdf
- Data
- NELL