Search Results for author:
Found 5 papers, 3 papers with code
Debiasing knowledge graph embeddings
It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers.
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking.
Ranked #1 on
Entity Linking
on WebQSP-WD
(using extra training data)
Improving Entity Disambiguation by Reasoning over a Knowledge Base
Recent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types.
Ranked #1 on
Entity Disambiguation
on ShadowLink-Top
Measuring Social Bias in Knowledge Graph Embeddings
It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks.
Merge and Label: A novel neural network architecture for nested NER
Named entity recognition (NER) is one of the best studied tasks in natural language processing.
Ranked #4 on
Nested Mention Recognition
on ACE 2005
