Search Results for author:
Found 5 papers, 3 papers with code
GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction
Document-level relation extraction aims to identify relationships between entities within a document.
Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare
KGs are used to discover diagnoses or prioritize genes relevant to disease, but they often rely on schemas that are not centred around a node or entity of interest, such as a person.
An Information Extraction Study: Take In Mind the Tokenization!
Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially.
Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
Though language model text embeddings have revolutionized NLP research, their ability to capture high-level semantic information, such as relations between entities in text, is limited.
Ranked #10 on
Relation Extraction
on Adverse Drug Events (ADE) Corpus
Automatic artifact removal of resting-state fMRI with Deep Neural Networks
In the course of fMRI, some sources of activation are caused by noise and artifacts.
