Abstract
Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.- Anthology ID:
- Q15-1023
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 3
- Month:
- Year:
- 2015
- Address:
- Cambridge, MA
- Editors:
- Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 315–328
- Language:
- URL:
- https://aclanthology.org/Q15-1023
- DOI:
- 10.1162/tacl_a_00141
- Cite (ACL):
- Xiao Ling, Sameer Singh, and Daniel S. Weld. 2015. Design Challenges for Entity Linking. Transactions of the Association for Computational Linguistics, 3:315–328.
- Cite (Informal):
- Design Challenges for Entity Linking (Ling et al., TACL 2015)
- PDF:
- https://preview.aclanthology.org/naacl24-info/Q15-1023.pdf
- Data
- FIGER