Abstract
Entity linking is a prominent thread of research focused on structured data creation by linking spans of text to an ontology or knowledge source. We revisit the use of structured prediction for entity linking which classifies each individual input token as an entity, and aggregates the token predictions. Our system, called SpEL (Structured prediction for Entity Linking) is a state-of-the-art entity linking system that uses some new ideas to apply structured prediction to the task of entity linking including: two refined fine-tuning steps; a context sensitive prediction aggregation strategy; reduction of the size of the model’s output vocabulary, and; we address a common problem in entity-linking systems where there is a training vs. inference tokenization mismatch. Our experiments show that we can outperform the state-of-the-art on the commonly used AIDA benchmark dataset for entity linking to Wikipedia. Our method is also very compute efficient in terms of number of parameters and speed of inference.- Anthology ID:
- 2023.emnlp-main.686
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11123–11137
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.686
- DOI:
- 10.18653/v1/2023.emnlp-main.686
- Cite (ACL):
- Hassan Shavarani and Anoop Sarkar. 2023. SpEL: Structured Prediction for Entity Linking. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11123–11137, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- SpEL: Structured Prediction for Entity Linking (Shavarani & Sarkar, EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.emnlp-main.686.pdf