8 citations · 22 across the 6 of their papers we have counts for
4 papers · 1 filter
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval
Megan Leszczynski, Daniel Y. Fu, Mayee F. Chen +1
Entity retrieval--retrieving information about entity mentions in a query--is a key step in open-domain tasks, such as question answering or fact checking. However, state-of-the-ar…
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
Maya Varma, Laurel Orr, Sen Wu +3
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare ent…
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
Laurel Orr, Megan Leszczynski, Simran Arora +4
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 t…
Understanding the Downstream Instability of Word Embeddings
Megan Leszczynski, Avner May, Jian Zhang +3
Many industrial machine learning (ML) systems require frequent retraining to keep up-to-date with constantly changing data. This retraining exacerbates a large challenge facing ML…