Selecting Key Views for Zero-Shot Entity Linking
Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Xiaojie Yuan, Wensheng Zhang
Abstract
Entity linking, which aligns mentions in the text to entities in knowledge bases, is essential for many natural language processing tasks. Considering the real-world scenarios, recent research hotspot of entity linking has focused on the zero-shot setting, where mentions need to link to unseen entities and only the description of each entity is provided. This task challenges the language understanding ability of models to capture the coherence evidence between the mention context and entity description. However, entity descriptions often contain rich information from multiple views, and a mention with context only relates to a small part of the information. Other irrelevant information will introduce noise, which interferes with models to make the right judgments. Furthermore, the existence of these information also makes it difficult to synthesize key information. To solve these problems, we select key views from descriptions and propose a KVZEL framework for zero-shot entity linking. Specifically, our KVZEL first adopts unsupervised clustering to form sub views. Then, it employs a mention-aware key views selection module to iteratively accumulate mention-focused views. This puts emphasis on capturing mention-related information and allows long-range key information integration. Finally, we aggregate key views to make the final decision. Experimental results show the effectiveness of our KVZEL and it achieves the new state-of-the-art on the zero-shot entity linking dataset.- Anthology ID:
- 2023.findings-emnlp.91
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1303–1312
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.91
- DOI:
- 10.18653/v1/2023.findings-emnlp.91
- Cite (ACL):
- Xuhui Sui, Ying Zhang, Kehui Song, Baohang Zhou, Xiaojie Yuan, and Wensheng Zhang. 2023. Selecting Key Views for Zero-Shot Entity Linking. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1303–1312, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Selecting Key Views for Zero-Shot Entity Linking (Sui et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-emnlp.91.pdf