1 citations · 1 across the 8 of their papers we have counts for
8 papers
Improving Information Extraction with Learned Queries
Omar Sharif, Soroush Vosoughi, Nikhil Singh
When information extraction fails, a natural instinct is to improve the model doing it: for example, by scaling it up or refining its reasoning. In this paper, we show that another…
Socially Constructed Treatment Plans: Analyzing Online Peer Interactions to Understand How Patients Navigate Complex Medical Conditions
Madhusudan Basak, Omar Sharif, Jessica Hulsey +4
When faced with complex and uncertain medical conditions (e.g., cancer, mental health conditions, recovery from substance dependency), millions of patients seek online peer support…
REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction
Omar Sharif, Joseph Gatto, Madhusudan Basak +1
Event argument extraction identifies arguments for predefined event roles in text. Existing work evaluates this task with exact match (EM), where predicted arguments must align exa…
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…
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…
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…
Chain-of-Thought Embeddings for Stance Detection on Social Media
Joseph Gatto, Omar Sharif, Sarah Masud Preum
Stance detection on social media is challenging for Large Language Models (LLMs), as emerging slang and colloquial language in online conversations often contain deeply implicit st…