Challenge: In the human body, various substances (entities) such as proteins and compounds interact and regulate each other, forming huge pathway networks.
Approach: They present a system that extracts and visualizes a disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis.
Outcome: The proposed system extracts and visualizes a disease network from biomedical articles on idiopathic pulmonary fibrosis (IPF) it includes two-dimensional (2D) and 3D visualizations of the constructed disease network.

Similar Papers

A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes (2022.lrec-1)

Copied to clipboard

Challenge: Biomedical researchers have used manual curation to extract biomedical interactions from research texts to improve coverage.
Approach: They propose a new dataset for training and evaluating multi-class multi-label biomedical relation extraction models using human annotations and the CTD database.
Outcome: The proposed dataset is substantially larger and cleaner than existing datasets and includes annotations linking mentions to their entities.
DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature (D19-57)

Copied to clipboard

Challenge: Identifying and understanding the pathogenesis of genetic diseases is an essential task.
Approach: They propose a joint deep learning model for gene mutation-disease knowledge extraction that adapts the state-of-the-art hierarchical multi-task learning framework for joint inference on named entity recognition and relation extraction.
Outcome: The proposed model achieves the average score of 0.45 on recognizing gene activities and disease entities and the average F1 score of 0.3 on extracting relations, ranking 1st in the AGAC RE task.
Biomedical relation extraction with pre-trained language representations and minimal task-specific architecture (D19-57)

Copied to clipboard

Challenge: Using a pre-trained BERT-Base model, we learn domain-specific language representations using biomedical text.
Approach: They propose a system that extends BERT, a state-of-the-art language model, which learns contextual language representations from a large unlabelled corpus.
Outcome: The proposed model outperforms a baseline model while relying on an extremely simple setup with no specially engineered features.
Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction (2021.findings-acl)

Copied to clipboard

Challenge: Existing models based on textual data do not capture context beyond the sentence.
Approach: They propose a framework that enables the model to learn multi-omnics biological information about entities (proteins) with the help of additional multi-modal cues like molecular structure.
Outcome: The proposed model is generalized and optimized for protein-protein interaction task and benefited from additional domain-specific cues.
INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Pre-Trained Language Models and Ensemble Learning (2025.naacl-srw)

Copied to clipboard

Challenge: InsightBuddy-AI is a system for extracting medication mentions and their associated attributes.
Approach: They propose a system for extracting medication mentions and their associated attributes . they use stacked and voting ensembles built upon pre-trained language models .
Outcome: The proposed system outperforms fine-tuned models in the extraction of medication mentions and associated attributes.
GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling (2025.findings-acl)

Copied to clipboard

Challenge: Document-level relation extraction (DocRE) solves problems of document quality . number of entities and entity-pair relations increases, causing incomplete annotations .
Approach: a framework that reduces the problem space using a graph-enhanced Transformer-based model is proposed . GLiM leverages large language models for reasoning to reduce the problem-space .
Outcome: GLiM boosts average recall and F1 scores on biomedical datasets . compared with existing models, GLim outperforms existing models on biomedicine benchmarks compared to existing models .
Leveraging Dependency Forest for Neural Medical Relation Extraction (D19-1)

Copied to clipboard

Challenge: Existing methods for medical relation extraction use dependency syntax as a source of features.
Approach: They propose a method to extract relational information from medical literature by using dependency forests.
Outcome: The proposed method outperforms the standard tree-based methods in the medical domain.
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)

Copied to clipboard

Challenge: Existing work deals with EL in the context of longer text, such as a sentence.
Approach: They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches.
Outcome: The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods.
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (2021.acl-long)

Copied to clipboard

Challenge: Existing models for medical named entity recognition and named entity normalization suffer from error propagation between the two tasks.
Approach: They propose an end-to-end progressive multi-task learning model for jointly modeling medical named entity recognition and normalization in an effective way.
Outcome: The proposed model reduces error propagation by exploiting the learnable features for both tasks to improve performance.
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019 (D19-57)

Copied to clipboard

Challenge: Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature.
Approach: They propose to use Named Entities to perform nested entities extraction, Entity Normalization and Relation Extraction to generalize the approach to different languages.
Outcome: The proposed approach can be generalized to different languages and showed it’s effectiveness for English and Spanish text.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations