Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition

Takuma Kato, Kaori Abe, Hiroki Ouchi, Shumpei Miyawaki, Jun Suzuki, Kentaro Inui

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.
Anthology ID:
2020.acl-srw.30
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2020
Address:
Online
Editors:
Shruti Rijhwani, Jiangming Liu, Yizhong Wang, Rotem Dror
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
222–229
Language:
URL:
https://aclanthology.org/2020.acl-srw.30
DOI:
10.18653/v1/2020.acl-srw.30
Bibkey:
Cite (ACL):
Takuma Kato, Kaori Abe, Hiroki Ouchi, Shumpei Miyawaki, Jun Suzuki, and Kentaro Inui. 2020. Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 222–229, Online. Association for Computational Linguistics.
Cite (Informal):
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition (Kato et al., ACL 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2020.acl-srw.30.pdf
Video:
 http://slideslive.com/38928673
Code
 katotakuma0501/Fine-grained-NER-models
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