Tell Me What You Read: Automatic Expertise-Based Annotator Assignment for Text Annotation in Expert Domains

Hiyori Yoshikawa, Tomoya Iwakura, Kimi Kaneko, Hiroaki Yoshida, Yasutaka Kumano, Kazutaka Shimada, Rafal Rzepka, Patrycja Swieczkowska


Abstract
This paper investigates the effectiveness of automatic annotator assignment for text annotation in expert domains. In the task of creating high-quality annotated corpora, expert domains often cover multiple sub-domains (e.g. organic and inorganic chemistry in the chemistry domain) either explicitly or implicitly. Therefore, it is crucial to assign annotators to documents relevant with their fine-grained domain expertise. However, most of existing methods for crowdsoucing estimate reliability of each annotator or annotated instance only after the annotation process. To address the issue, we propose a method to estimate the domain expertise of each annotator before the annotation process using information easily available from the annotators beforehand. We propose two measures to estimate the annotator expertise: an explicit measure using the predefined categories of sub-domains, and an implicit measure using distributed representations of the documents. The experimental results on chemical name annotation tasks show that the annotation accuracy improves when both explicit and implicit measures for annotator assignment are combined.
Anthology ID:
2021.ranlp-1.177
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1575–1585
Language:
URL:
https://aclanthology.org/2021.ranlp-1.177
DOI:
Bibkey:
Cite (ACL):
Hiyori Yoshikawa, Tomoya Iwakura, Kimi Kaneko, Hiroaki Yoshida, Yasutaka Kumano, Kazutaka Shimada, Rafal Rzepka, and Patrycja Swieczkowska. 2021. Tell Me What You Read: Automatic Expertise-Based Annotator Assignment for Text Annotation in Expert Domains. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1575–1585, Held Online. INCOMA Ltd..
Cite (Informal):
Tell Me What You Read: Automatic Expertise-Based Annotator Assignment for Text Annotation in Expert Domains (Yoshikawa et al., RANLP 2021)
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PDF:
https://preview.aclanthology.org/naacl24-info/2021.ranlp-1.177.pdf