Papers with ADE
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (2020.coling-main)
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| Challenge: | Existing methods treat each span token equally important, ignoring significant features. |
| Approach: | They propose a span-based joint extraction framework with attention-based semantic representations that utilizes span-specific and contextual representations. |
| Outcome: | The proposed model outperforms existing models on ACE2005, CoNLL2004 and ADE. |
QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis (2024.emnlp-industry)
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| Challenge: | Exploratory Data Analysis (EDA) is a complex task that requires thorough exploration and analysis of the data. |
| Approach: | They propose a fully automated EDA system that generates questions in iterations and analyzes data to produce multiple relevant insights. |
| Outcome: | QUIS is a fully automated EDA system that generates questions in iterations without human intervention or manually curated examples. |
BERT Prescriptions to Avoid Unwanted Headaches: A Comparison of Transformer Architectures for Adverse Drug Event Detection (2021.eacl-main)
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| Challenge: | Pretrained transformer-based models are a common choice for identifying drug events from social media texts. |
| Approach: | They propose to compare transformer-based models with in-domain language pretraining to find out which one is better at ADE detection. |
| Outcome: | The proposed models outperform SpanBERT and PubMedBERT on two benchmarks. |
Attribution-Guided Multi-Object Hallucination and Bias Detection in Vision-Language Models (2026.eacl-long)
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| Challenge: | Existing methods struggle with multi-object grounding because language priors dominate visual evidence, causing hallucinated or biased objects to produce attention distributions or similarity scores nearly indistinguishable from those of real objects. |
| Approach: | They propose a Shapley value-based attribution framework that uses Kernel SHAP and multi-layer fusion to detect hallucinated and biased objects. |
| Outcome: | Evaluated on ADE and COCO datasets, SHAPLENS improves hallucination detection accuracy by 8–12% and F1 by 10–14% over baselines. |
A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events (2020.findings-emnlp)
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| Challenge: | Adverse drug events (ADEs) are a leading cause of death in the United States and cost around $30 $130 billion every year. |
| Approach: | They propose a multi-grained joint deep network to learn ADE entity recognition and ADE sentence classification tasks. |
| Outcome: | The proposed model improves state-of-art F1 score on the MADE 1.0 benchmark of EHR notes. |
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference (2021.acl-long)
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| Challenge: | Existing methods for information extraction from biomedical texts do not utilize external knowledge . despite the exponential growth of biomedically published articles, many existing methods fall behind . |
| Approach: | They propose a framework that utilizes external knowledge for entity and relation extraction . KECI uses an initial span graph to construct a knowledge graph containing relevant background knowledge . |
| Outcome: | The proposed framework achieves state-of-the-art results in two biomedical datasets . it achieves 4.59% and 4.91% improvement in F1 scores over the state- of-the art methods . |
Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)
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| Challenge: | Dual encoders have been used for question-answering and information retrieval tasks with good results. |
| Approach: | They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers . |
| Outcome: | The proposed architectures outperform SDE and ADE on QA retrieval tasks. |
Enhancing Adverse Drug Event Detection with Multimodal Dataset: Corpus Creation and Model Development (2024.findings-acl)
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| Challenge: | ADEs are a serious public health concern and cost healthcare systems billions of dollars . despite advancements in healthcare, ADE detection remains a significant challenge . |
| Approach: | They propose a multimodal adverse drug event detection dataset that merges ADE-related textual information with visual aids to enhance patient safety. |
| Outcome: | The proposed dataset integrates ADE-related textual information with visual aids to improve patient safety and healthcare accessibility. |
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance (2023.findings-emnlp)
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Karel D’Oosterlinck, François Remy, Johannes Deleu, Thomas Demeester, Chris Develder, Klim Zaporojets, Aneiss Ghodsi, Simon Ellershaw, Jack Collins, Christopher Potts
| Challenge: | pharmacovigilance (PV) is a tool for analyzing adverse drug events from biomedical literature . pharmacologists use natural language processing to extract core information from papers . |
| Approach: | They propose a resource for biomedical adverse drug event eXtraction using natural language processing. |
| Outcome: | The proposed model achieves 59.1% F1 (validation) and estimates human performance to be 72.0% F1 . the proposed model could be used to improve drug safety monitoring, also called pharmacovigilance, in the future. |