38 papers with code • 8 benchmarks • 8 datasets
Entity Disambiguation is the task of linking mentions of ambiguous entities to their referent entities in a knowledge base such as Wikipedia.
The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words.
Ranked #4 on Entity Disambiguation on TAC2010
The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.
Ranked #1 on Entity Disambiguation on TAC2010
Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time.
Ranked #2 on Entity Disambiguation on Mewsli-9 (using extra training data)
We propose a new global entity disambiguation (ED) model based on contextualized embeddings of words and entities.
Ranked #1 on Entity Disambiguation on MSNBC
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the training data, termed tail entities.
Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood.