44 citations · 63 across the 11 of their papers we have counts for
5 papers · 1 filter
Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021
Adrian Ahne, Vivek Khetan, Xavier Tannier +6
Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a to…
Template Filling for Controllable Commonsense Reasoning
Dheeraj Rajagopal, Vivek Khetan, Bogdan Sacaleanu +3
Large-scale sequence-to-sequence models have shown to be adept at both multiple-choice and open-domain commonsense reasoning tasks. However, the current systems do not provide the…
MIMICause: Representation and automatic extraction of causal relation types from clinical notes
Vivek Khetan, Md Imbesat Hassan Rizvi, Jessica Huber +3
Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combin…
Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights
Vivek Khetan, Annervaz K M, Erin Wetherley +3
The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner. For businesses in domains with rapidly changing rules…
Causal BERT : Language models for causality detection between events expressed in text
Vivek Khetan, Roshni Ramnani, Mayuresh Anand +2
Causality understanding between events is a critical natural language processing task that is helpful in many areas, including health care, business risk management and finance. On…