A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy
Genady Beryozkin, Yoel Drori, Oren Gilon, Tzvika Hartman, Idan Szpektor · 2019
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.