Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing

Yadollah Yaghoobzadeh, Hinrich Schütze


Abstract
Accurate and complete knowledge bases (KBs) are paramount in NLP. We employ mul-itiview learning for increasing the accuracy and coverage of entity type information in KBs. We rely on two metaviews: language and representation. For language, we consider high-resource and low-resource languages from Wikipedia. For representation, we consider representations based on the context distribution of the entity (i.e., on its embedding), on the entity’s name (i.e., on its surface form) and on its description in Wikipedia. The two metaviews language and representation can be freely combined: each pair of language and representation (e.g., German embedding, English description, Spanish name) is a distinct view. Our experiments on entity typing with fine-grained classes demonstrate the effectiveness of multiview learning. We release MVET, a large multiview — and, in particular, multilingual — entity typing dataset we created. Mono- and multilingual fine-grained entity typing systems can be evaluated on this dataset.
Anthology ID:
D18-1343
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3060–3066
Language:
URL:
https://aclanthology.org/D18-1343
DOI:
10.18653/v1/D18-1343
Bibkey:
Cite (ACL):
Yadollah Yaghoobzadeh and Hinrich Schütze. 2018. Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3060–3066, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing (Yaghoobzadeh & Schütze, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1343.pdf
Attachment:
 D18-1343.Attachment.zip
Code
 yyaghoobzadeh/MVET
Data
FIGERFigment