Search Results for author:
Found 21 papers, 7 papers with code
How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?
As such, generative LMs pretrained on biomedical corpora have proliferated and biomedical instruction finetuning has been attempted as well, all with the hope that domain specificity improves performance on downstream tasks.
Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a single sentence is input.
Comparison of pipeline, sequence-to-sequence, and GPT models for end-to-end relation extraction: experiments with the rare disease use-case
Our contribution is also the first to conduct E2ERE for the RareDis dataset.
End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies
End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially.
Chemical-Protein Interaction Extraction
named-entity-recognition
+4
End-to-End $n$-ary Relation Extraction for Combination Drug Therapies
Extracting combination therapies from scientific literature inherently constitutes an $n$-ary relation extraction problem.
COVID-19 event extraction from Twitter via extractive question answering with continuous prompts
As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it.
Deep neural networks for fine-grained surveillance of overdose mortality
Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death.
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)
Although we use COVID-19 as a use case in this effort, our framework is general enough to be applied to other domains of interest in clinical NLP.
Predicting Opioid Use Disorder from Longitudinal Healthcare Data using Multi-stream Transformer
Opioid Use Disorder (OUD) is a public health crisis costing the US billions of dollars annually in healthcare, lost workplace productivity, and crime.
Improved Biomedical Word Embeddings in the Transformer Era
This fine-tuning is accomplished with the BERT transformer architecture in the two-sentence input mode with a classification objective that captures MeSH pair co-occurrence.
Literature Retrieval for Precision Medicine with Neural Matching and Faceted Summarization
Component (a) directly generates a matching score of a candidate document for a query.
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples.
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.
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.
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
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces
Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III.
EMR Coding with Semi-Parametric Multi-Head Matching Networks
Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends.
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.
Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models
Text mining the relations between chemicals and proteins is an increasingly important task.
Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept Embeddings
In this paper, we employ knowledge-based approaches that also exploit recent advances in neural word/concept embeddings to improve over the state-of-the-art in biomedical WSD using the MSH WSD dataset as the test set.
