Medical Relation Extraction

9 papers with code • 2 benchmarks • 5 datasets

Biomedical relation extraction is the task of detecting and classifying semantic relationships from biomedical text.

Most implemented papers

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

dmis-lab/biobert 25 Jan 2019

Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.

Crowdsourcing Ground Truth for Medical Relation Extraction

CrowdTruth/Medical-Relation-Extraction 9 Jan 2017

Cognitive computing systems require human labeled data for evaluation, and often for training.

Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network

sunilitggu/DDI-extraction-through-LSTM 28 Jan 2017

The two models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the output of Bi-LSTM layer to assign weights to features.

A hybrid deep learning approach for medical relation extraction

RaghavendraCh/RelationExtraction_keras 26 Jun 2018

Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain.

Leveraging Dependency Forest for Neural Medical Relation Extraction

freesunshine0316/dep-forest-re IJCNLP 2019

Medical relation extraction discovers relations between entity mentions in text, such as research articles.

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

cbluebenchmark/cblue ACL 2022

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.

LinkBERT: Pretraining Language Models with Document Links

michiyasunaga/LinkBERT ACL 2022

Language model (LM) pretraining can learn various knowledge from text corpora, helping downstream tasks.

Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

jyf123/CP-GCN COLING 2022

However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate.