Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias

Christin Beck, Hannah Booth, Mennatallah El-Assady, Miriam Butt

Abstract
The development of linguistic corpora is fraught with various problems of annotation and representation. These constitute a very real challenge for the development and use of annotated corpora, but as yet not much literature exists on how to address the underlying problems. In this paper, we identify and discuss five sources of representation problems, which are independent though interrelated: ambiguity, variation, uncertainty, error and bias. We outline and characterize these sources, discussing how their improper treatment can have stark consequences for research outcomes. Finally, we discuss how an adequate treatment can inform corpus-related linguistic research, both computational and theoretical, improving the reliability of research results and NLP models, as well as informing the more general reproducibility issue.
Anthology ID:
2020.law-1.6
Volume:
Proceedings of the 14th Linguistic Annotation Workshop
Month:
December
Year:
2020
Address:
Barcelona, Spain
Venue:
LAW
SIG:
SIGANN
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–73
Language:
URL:
https://aclanthology.org/2020.law-1.6
DOI:
Bibkey:
Cite (ACL):
Christin Beck, Hannah Booth, Mennatallah El-Assady, and Miriam Butt. 2020. Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias. In Proceedings of the 14th Linguistic Annotation Workshop, pages 60–73, Barcelona, Spain. Association for Computational Linguistics.
Cite (Informal):
Representation Problems in Linguistic Annotations: Ambiguity, Variation, Uncertainty, Error and Bias (Beck et al., LAW 2020)
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