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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2021.textgraphs-1.14.pdf
- Code
- webersab/german_general_entity_typing
- Data
- FIGER