A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy

Genady Beryozkin, Yoel Drori, Oren Gilon, Tzvika Hartman, Idan Szpektor


Abstract
We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. Yet, the relations between all tags are provided in a tag hierarchy, covering the test tags as a combination of training tags. This setting occurs when various datasets are created using different annotation schemes. This is also the case of extending a tag-set with a new tag by annotating only the new tag in a new dataset. We propose to use the given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. We compare this model to combining independent models and to a model based on the multitasking approach. Our experiments show the benefit of the tag-hierarchy model, especially when facing non-trivial consolidation of tag-sets.
Anthology ID:
P19-1014
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
140–150
Language:
URL:
https://aclanthology.org/P19-1014
DOI:
10.18653/v1/P19-1014
Bibkey:
Cite (ACL):
Genady Beryozkin, Yoel Drori, Oren Gilon, Tzvika Hartman, and Idan Szpektor. 2019. A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 140–150, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy (Beryozkin et al., ACL 2019)
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