Search Results for author:
Found 39 papers, 14 papers with code
Evidence-based Fact-Checking of Health-related Claims
Our experiments showed that training deep learning models on real-world medical claims greatly improves performance compared to models trained on synthetic and open-domain claims.
Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering
The shared task addressed two of the challenges faced by medical video question answering: (I) a video classification task that explores new approaches to medical video understanding (labeling), and (ii) a visual answer localization task.
Enhancing Question Answering by Injecting Ontological Knowledge through Regularization
Deep neural networks have demonstrated high performance on many natural language processing (NLP) tasks that can be answered directly from text, and have struggled to solve NLP tasks requiring external (e. g., world) knowledge.
Overview of the MEDIQA 2021 Shared Task on Summarization in the Medical Domain
The MEDIQA 2021 shared tasks at the BioNLP 2021 workshop addressed three tasks on summarization for medical text: (i) a question summarization task aimed at exploring new approaches to understanding complex real-world consumer health queries, (ii) a multi-answer summarization task that targeted aggregation of multiple relevant answers to a biomedical question into one concise and relevant answer, and (iii) a radiology report summarization task addressing the development of clinically relevant impressions from radiology report findings.
Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches
Toward this, this paper is focused on answering health-related questions asked by the public by providing visual answers from medical videos.
Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking
Pre-trained language models (PLMs) have proven to be effective for document re-ranking task.
A Dataset for Plain Language Adaptation of Biomedical Abstracts
Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature.
Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features
We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets.
Clinical Language Understanding Evaluation (CLUE)
Clinical language processing has received a lot of attention in recent years, resulting in new models or methods for disease phenotyping, mortality prediction, and other tasks.
CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization
The quest for seeking health information has swamped the web with consumers' health-related questions.
A Dataset for Medical Instructional Video Classification and Question Answering
This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions.
Reinforcement Learning for Abstractive Question Summarization with Question-aware Semantic Rewards
The growth of online consumer health questions has led to the necessity for reliable and accurate question answering systems.
Question-aware Transformer Models for Consumer Health Question Summarization
In this paper, we study the task of abstractive summarization for real-world consumer health questions.
Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID
We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19.
Flight of the PEGASUS? Comparing Transformers on Few-shot and Zero-shot Multi-document Abstractive Summarization
Recent work has shown that pre-trained Transformers obtain remarkable performance on many natural language processing tasks including automatic summarization.
HOLMS: Alternative Summary Evaluation with Large Language Models
Efficient document summarization requires evaluation measures that can not only rank a set of systems based on an average score, but also highlight which individual summary is better than another.
Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning
Automatic summarization research has traditionally focused on providing high quality general-purpose summaries of documents.
Visual Question Generation from Radiology Images
Visual Question Generation (VQG), the task of generating a question based on image contents, is an increasingly important area that combines natural language processing and computer vision.
Question-Driven Summarization of Answers to Consumer Health Questions
This dataset can be used to evaluate single or multi-document summaries generated by algorithms using extractive or abstractive approaches.
TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic.
Bridging the Knowledge Gap: Enhancing Question Answering with World and Domain Knowledge
In this paper we present OSCAR (Ontology-based Semantic Composition Augmented Regularization), a method for injecting task-agnostic knowledge from an Ontology or knowledge graph into a neural network during pretraining.
Understanding Spatial Language in Radiology: Representation Framework, Annotation, and Spatial Relation Extraction from Chest X-ray Reports using Deep Learning
We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL).
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain.
On the Summarization of Consumer Health Questions
In this paper, we study neural abstractive models for medical question summarization.
A Question-Entailment Approach to Question Answering
One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions.
A dataset of clinically generated visual questions and answers about radiology images
We introduce VQA-RAD, the first manually constructed dataset where clinicians asked naturally occurring questions about radiology images and provided reference answers.
NLM\_NIH at SemEval-2017 Task 3: from Question Entailment to Question Similarity for Community Question Answering
Tested on cQA-B-2016 test data, our RQE system outperformed the best system of the 2016 challenge in all measures with 77. 47 MAP and 80. 57 Accuracy.
TextFlow: A Text Similarity Measure based on Continuous Sequences
Text similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment.
A Hybrid Approach to Generation of Missing Abstracts in Biomedical Literature
Readers usually rely on abstracts to identify relevant medical information from scientific articles.
Annotating Named Entities in Consumer Health Questions
We describe a corpus of consumer health questions annotated with named entities.
Annotating Logical Forms for EHR Questions
From these, 468 specific questions are found containing 259 unique medical concepts and requiring 53 unique predicates to represent the logical forms.
Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation
Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.
Annotating Question Decomposition on Complex Medical Questions
This paper presents a method for annotating question decomposition on complex medical questions.
