Investigation on Data Adaptation Techniques for Neural Named Entity Recognition

Evgeniia Tokarchuk, David Thulke, Weiyue Wang, Christian Dugast, Hermann Ney


Abstract
Data processing is an important step in various natural language processing tasks. As the commonly used datasets in named entity recognition contain only a limited number of samples, it is important to obtain additional labeled data in an efficient and reliable manner. A common practice is to utilize large monolingual unlabeled corpora. Another popular technique is to create synthetic data from the original labeled data (data augmentation). In this work, we investigate the impact of these two methods on the performance of three different named entity recognition tasks.
Anthology ID:
2021.acl-srw.1
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–15
Language:
URL:
https://aclanthology.org/2021.acl-srw.1
DOI:
10.18653/v1/2021.acl-srw.1
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-srw.1.pdf