Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection

Young Min Cho, Li Zhang, Chris Callison-Burch


Abstract
Entity linking, the task of linking potentially ambiguous mentions in texts to corresponding knowledge-base entities, is an important component for language understanding. We address two challenge in entity linking: how to leverage wider contexts surrounding a mention, and how to deal with limited training data. We propose a fully unsupervised model called SumMC that first generates a guided summary of the contexts conditioning on the mention, and then casts the task to a multiple-choice problem where the model chooses an entity from a list of candidates. In addition to evaluating our model on existing datasets that focus on named entities, we create a new dataset that links noun phrases from WikiHow to Wikidata. We show that our SumMC model achieves state-of-the-art unsupervised performance on our new dataset and on exiting datasets.
Anthology ID:
2022.emnlp-main.638
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9394–9401
Language:
URL:
https://aclanthology.org/2022.emnlp-main.638
DOI:
10.18653/v1/2022.emnlp-main.638
Bibkey:
Cite (ACL):
Young Min Cho, Li Zhang, and Chris Callison-Burch. 2022. Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9394–9401, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection (Cho et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.638.pdf