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pro vyhledávání: '"Sharif, Omar"'
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 document. In this study, we revisit this definition of Event Extract
Externí odkaz:
http://arxiv.org/abs/2410.03594
Autor:
Basak, Madhusudan, Sharif, Omar, Lord, Sarah E., Borodovsky, Jacob T., Marsch, Lisa A., Springer, Sandra A., Nunes, Edward, Brackett, Charlie D., ArchiBald, Luke J., Preum, Sarah M.
Background: One of the key FDA-approved medications for Opioid Use Disorder (OUD) is buprenorphine. Despite its popularity, individuals often report various information needs regarding buprenorphine treatment on social media platforms like Reddit. Ho
Externí odkaz:
http://arxiv.org/abs/2410.01633
Autor:
Miller, Elliot M., Chan, Tat Chung D., Montes-Matamoros, Carlos, Sharif, Omar, Pujo-Menjouet, Laurent, Lindstrom, Michael R.
Many neurodegenerative diseases (NDs) are characterized by the slow spatial spread of toxic protein species in the brain. The toxic proteins can induce neuronal stress, triggering the Unfolded Protein Response (UPR), which slows or stops protein tran
Externí odkaz:
http://arxiv.org/abs/2405.16695
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-like answers to fact-driven social media questions is still under-explo
Externí odkaz:
http://arxiv.org/abs/2404.01147
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 entertainment, memes can also disseminate hateful content targeting individua
Externí odkaz:
http://arxiv.org/abs/2403.10829
Large Language Models for Document-Level Event-Argument Data Augmentation for Challenging Role Types
Event Argument Extraction (EAE) is an extremely difficult information extraction problem -- with significant limitations in few-shot cross-domain (FSCD) settings. A common solution to FSCD modeling is data augmentation. Unfortunately, existing augmen
Externí odkaz:
http://arxiv.org/abs/2403.03304
Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and tex
Externí odkaz:
http://arxiv.org/abs/2402.09738
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 stance labels. Chain-of-Thought (COT) prompting has recently been shown
Externí odkaz:
http://arxiv.org/abs/2310.19750
Amidst the sharp rise in the evaluation of large language models (LLMs) on various tasks, we find that semantic textual similarity (STS) has been under-explored. In this study, we show that STS can be cast as a text generation problem while maintaini
Externí odkaz:
http://arxiv.org/abs/2309.06541