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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W17-1417.pdf
Data
NELL