Fine-grained General Entity Typing in German using GermaNet

Sabine Weber, Mark Steedman


Abstract
Fine-grained entity typing is important to tasks like relation extraction and knowledge base construction. We find however, that fine-grained entity typing systems perform poorly on general entities (e.g. “ex-president”) as compared to named entities (e.g. “Barack Obama”). This is due to a lack of general entities in existing training data sets. We show that this problem can be mitigated by automatically generating training data from WordNets. We use a German WordNet equivalent, GermaNet, to automatically generate training data for German general entity typing. We use this data to supplement named entity data to train a neural fine-grained entity typing system. This leads to a 10% improvement in accuracy of the prediction of level 1 FIGER types for German general entities, while decreasing named entity type prediction accuracy by only 1%.
Anthology ID:
2021.textgraphs-1.14
Volume:
Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
Month:
June
Year:
2021
Address:
Mexico City, Mexico
Editors:
Alexander Panchenko, Fragkiskos D. Malliaros, Varvara Logacheva, Abhik Jana, Dmitry Ustalov, Peter Jansen
Venue:
TextGraphs
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
138–143
Language:
URL:
https://aclanthology.org/2021.textgraphs-1.14
DOI:
10.18653/v1/2021.textgraphs-1.14
Bibkey:
Cite (ACL):
Sabine Weber and Mark Steedman. 2021. Fine-grained General Entity Typing in German using GermaNet. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15), pages 138–143, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Fine-grained General Entity Typing in German using GermaNet (Weber & Steedman, TextGraphs 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.textgraphs-1.14.pdf
Code
 webersab/german_general_entity_typing
Data
FIGER