no code implementations • 29 Apr 2024 • Yupeng Cao, Zhi Chen, Qingyun Pei, Prashant Kumar, K. P. Subbalakshmi, Papa Momar Ndiaye
In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock performance is a critical challenge that has attracted both academics and investors.
no code implementations • 11 Apr 2024 • Yupeng Cao, Zhi Chen, Qingyun Pei, Fabrizio Dimino, Lorenzo Ausiello, Prashant Kumar, K. P. Subbalakshmi, Papa Momar Ndiaye
Through comparative experiments, we demonstrate how different data sources contribute to financial risk assessment and discuss the critical role of LLMs in this context.
no code implementations • 22 Feb 2024 • Yupeng Cao, Aishwarya Muralidharan Nair, Elyon Eyimife, Nastaran Jamalipour Soofi, K. P. Subbalakshmi, John R. Wullert II, Chumki Basu, David Shallcross
The central research question of this paper is whether it is possible to use large language models (LLMs) to detect misinformation in scientific reporting.
no code implementations • 31 Oct 2022 • Bingyang Wen, K. P. Subbalakshmi, Fan Yang
Attention mechanisms have recently demonstrated impressive performance on a range of NLP tasks, and attention scores are often used as a proxy for model explainability.
no code implementations • 14 Sep 2021 • Mingxuan Chen, Xinqiao Chu, K. P. Subbalakshmi
We also provide a novel architecture that classifies the news data into misinformation or truth to provide a baseline performance for this dataset.
no code implementations • NAACL (CLPsych) 2021 • Ning Wang, Fan Luo, Yuvraj Shivtare, Varsha D. Badal, K. P. Subbalakshmi, R. Chandramouli, Ellen Lee
We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CLPsych 2021 shared task.
no code implementations • 21 Apr 2021 • Bingyang Wen, Luis Oliveros Colon, K. P. Subbalakshmi, R. Chandramouli
Though there are prior works that have demonstrated great progress, most of them learn the correlations in the data distributions rather than the true processes in which the datasets are naturally generated.
no code implementations • 21 Jul 2020 • Mingxuan Chen, Ning Wang, K. P. Subbalakshmi
With social media becoming ubiquitous, information consumption from this media has also increased.
no code implementations • 25 Jun 2020 • Ning Wang, Mingxuan Chen, K. P. Subbalakshmi
In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer`s disease based on their language abilities.
no code implementations • WS 2020 • Ning Wang, Fan Luo, Vishal Peddagangireddy, K. P. Subbalakshmi, R. Chandramouli
In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer`s disease.