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Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels
As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination of drug safety information.
Neural Metric Learning for Fast End-to-End Relation Extraction
We introduce a novel neural architecture utilizing the table structure, based on repeated applications of 2D convolutions for pooling local dependency and metric-based features, that improves on the state-of-the-art without the need for global optimization.
Ranked #6 on
Relation Extraction
on CoNLL04
A Multi-Task Learning Framework for Extracting Drugs and Their Interactions from Drug Labels
As drug-drug interactions (DDIs) may cause adverse reactions, being able to extracting DDIs from drug labels into machine-readable form is an important effort in effectively deploying drug safety information.
Predicting Psychological Health from Childhood Essays with Convolutional Neural Networks for the CLPsych 2018 Shared Task (Team UKNLP)
The second task (task B) asks participants to predict future psychological distress at ages 23, 33, 42, and 50 using the age 11 essays.
