@inproceedings{kato-etal-2020-embeddings,
title = "Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition",
author = "Kato, Takuma and
Abe, Kaori and
Ouchi, Hiroki and
Miyawaki, Shumpei and
Suzuki, Jun and
Inui, Kentaro",
editor = "Rijhwani, Shruti and
Liu, Jiangming and
Wang, Yizhong and
Dror, Rotem",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-srw.30",
doi = "10.18653/v1/2020.acl-srw.30",
pages = "222--229",
abstract = "In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.",
}
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<abstract>In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.</abstract>
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%0 Conference Proceedings
%T Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
%A Kato, Takuma
%A Abe, Kaori
%A Ouchi, Hiroki
%A Miyawaki, Shumpei
%A Suzuki, Jun
%A Inui, Kentaro
%Y Rijhwani, Shruti
%Y Liu, Jiangming
%Y Wang, Yizhong
%Y Dror, Rotem
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F kato-etal-2020-embeddings
%X In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.
%R 10.18653/v1/2020.acl-srw.30
%U https://aclanthology.org/2020.acl-srw.30
%U https://doi.org/10.18653/v1/2020.acl-srw.30
%P 222-229
Markdown (Informal)
[Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition](https://aclanthology.org/2020.acl-srw.30) (Kato et al., ACL 2020)
ACL