4 citations · 13 across the 26 of their papers we have counts for
9 papers · 1 filter
In-Context Learning for Preserving Patient Privacy: A Framework for Synthesizing Realistic Patient Portal Messages
Joseph Gatto, Parker Seegmiller, Timothy E. Burdick +1
Since the COVID-19 pandemic, clinicians have seen a large and sustained influx in patient portal messages, significantly contributing to clinician burnout. To the best of our knowl…
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments
Omar Sharif, Joseph Gatto, Madhusudan Basak +1
Prior works formulate the extraction of event-specific arguments as a span extraction problem, where event arguments are explicit -- i.e. assumed to be contiguous spans of text in…
A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product
Madhusudan Basak, Omar Sharif, Sarah E. Lord +7
Background: One of the key FDA-approved medications for Opioid Use Disorder (OUD) is buprenorphine. Despite its popularity, individuals often report various information needs regar…
Depth : Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic Generalizability
Parker Seegmiller, Joseph Gatto, Sarah Masud Preum
Recent evaluations of cross-domain text classification models aim to measure the ability of a model to obtain domain-invariant performance in a target domain given labeled samples…
Do LLMs Find Human Answers To Fact-Driven Questions Perplexing? A Case Study on Reddit
Parker Seegmiller, Joseph Gatto, Omar Sharif +2
Large language models (LLMs) have been shown to be proficient in correctly answering questions in the context of online discourse. However, the study of using LLMs to model human-l…
Scope of Large Language Models for Mining Emerging Opinions in Online Health Discourse
Joseph Gatto, Madhusudan Basak, Yash Srivastava +2
In this paper, we develop an LLM-powered framework for the curation and evaluation of emerging opinion mining in online health communities. We formulate emerging opinion mining as…
Deciphering Hate: Identifying Hateful Memes and Their Targets
Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque +1
Internet memes have become a powerful means for individuals to express emotions, thoughts, and perspectives on social media. While often considered as a source of humor and enterta…
Large Language Models for Document-Level Event-Argument Data Augmentation for Challenging Role Types
Joseph Gatto, Parker Seegmiller, Omar Sharif +1
Event Argument Extraction (EAE) is an extremely difficult information extraction problem -- with significant limitations in few-shot cross-domain (FSCD) settings. A common solution…
Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection
Eftekhar Hossain, Omar Sharif, Mohammed Moshiul Hoque +1
Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal represe…