In Media Res: A Corpus for Evaluating Named Entity Linking with Creative Works

Adrian M.P. Brasoveanu, Albert Weichselbraun, Lyndon Nixon


Abstract
Annotation styles express guidelines that direct human annotators in what rules to follow when creating gold standard annotations of text corpora. These guidelines not only shape the gold standards they help create, but also influence the training and evaluation of Named Entity Linking (NEL) tools, since different annotation styles correspond to divergent views on the entities present in the same texts. Such divergence is particularly present in texts from the media domain that contain references to creative works. In this work we present a corpus of 1000 annotated documents selected from the media domain. Each document is presented with multiple gold standard annotations representing various annotation styles. This corpus is used to evaluate a series of Named Entity Linking tools in order to understand the impact of the differences in annotation styles on the reported accuracy when processing highly ambiguous entities such as names of creative works. Relaxed annotation guidelines that include overlap styles lead to better results across all tools.
Anthology ID:
2020.conll-1.28
Volume:
Proceedings of the 24th Conference on Computational Natural Language Learning
Month:
November
Year:
2020
Address:
Online
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
355–364
Language:
URL:
https://aclanthology.org/2020.conll-1.28
DOI:
10.18653/v1/2020.conll-1.28
Bibkey:
Cite (ACL):
Adrian M.P. Brasoveanu, Albert Weichselbraun, and Lyndon Nixon. 2020. In Media Res: A Corpus for Evaluating Named Entity Linking with Creative Works. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 355–364, Online. Association for Computational Linguistics.
Cite (Informal):
In Media Res: A Corpus for Evaluating Named Entity Linking with Creative Works (Brasoveanu et al., CoNLL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.conll-1.28.pdf
Code
 modultechnology/in_media_res