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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.638.pdf