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        <title>A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy</title>
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        <namePart type="given">Genady</namePart>
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        <namePart type="given">Yoel</namePart>
        <namePart type="family">Drori</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Idan</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1014</identifier>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy
%A Beryozkin, Genady
%A Drori, Yoel
%A Gilon, Oren
%A Hartman, Tzvika
%A Szpektor, Idan
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F beryozkin-etal-2019-joint
%X 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.
%R 10.18653/v1/P19-1014
%U https://aclanthology.org/P19-1014
%U https://doi.org/10.18653/v1/P19-1014
%P 140-150

Markdown (Informal)

[A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy](https://aclanthology.org/P19-1014) (Beryozkin et al., ACL 2019)

• A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy (Beryozkin et al., ACL 2019)

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.