Contrastive Entity Coreference and Disambiguation for Historical Texts
By Abhishek Arora, Emily Silcock, Leander Heldring, Melissa Dell
Rating
1136
Battle Count: 103
Relevance
3/10
While not directly applicable to quantitative trading, the entity disambiguation techniques could potentially be adapted for processing financial news or reports
Implementation Complexity
7/10
Requires understanding of contrastive learning and bi-encoder architectures, as well as significant data processing
Reproducibility
4/5
Code and datasets are made publicly available, but some computational resources may be required for full reproduction
About this paper
Methodology: Contrastive Learning. Problem types: Natural Language Processing, Entity Disambiguation, Coreference Resolution.
The interactive Everscope explorer (charts, battles, favorites) loads below.