V
Vivek Khetan
Researcher at Accenture
Publications - 13
Citations - 217
Vivek Khetan is an academic researcher from Accenture. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 4, co-authored 9 publications receiving 153 citations. Previous affiliations of Vivek Khetan include University of Texas at Austin.
Papers
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Journal ArticleDOI
Neural information retrieval: at the end of the early years
Kezban Dilek Onal,Kezban Dilek Onal,Ye Zhang,Ismail Sengor Altingovde,Md. Mustafizur Rahman,Pinar Karagoz,Alexander Braylan,Brandon Dang,Heng-Lu Chang,Henna Kim,Quinten McNamara,Aaron Angert,Edward Banner,Vivek Khetan,Tyler McDonnell,An Thanh Nguyen,Dan Xu,Byron C. Wallace,Maarten de Rijke,Matthew Lease +19 more
TL;DR: The successes of neural IR thus far are highlighted, obstacles to its wider adoption are cataloged, and potentially promising directions for future research are suggested.
Posted Content
Neural Information Retrieval: A Literature Review
Ye Zhang,Md. Mustafizur Rahman,Alexander Braylan,Brandon Dang,Heng-Lu Chang,Henna Kim,Quinten McNamara,Aaron Angert,Edward Banner,Vivek Khetan,Tyler McDonnell,An Thanh Nguyen,Dan Xu,Byron C. Wallace,Matthew Lease +14 more
TL;DR: The current landscape of Neural IR research is surveyed, paying special attention to the use of learned representations of queries and documents (i.e., neural embeddings), to highlight the successes and obstacles to its wider adoption, and suggest potentially promising directions for future research.
Book ChapterDOI
Causal BERT: Language Models for Causality Detection Between Events Expressed in Text
TL;DR: The language model's capabilities for causal association among events expressed in natural language text are investigated using sentence context combined with event information, and by leveraging masked event context with in-domain and out-of-domain data distribution.
Journal ArticleDOI
RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media
TL;DR: A new method to automatically derive (noisy) supervision for this task is proposed which is used to train a dense retrieval model; this outperforms baseline models.
Journal ArticleDOI
Extraction of Explicit and Implicit Cause-Effect Relationships in Patient-Reported Diabetes-Related Tweets From 2017 to 2021: Deep Learning Approach
Adrian Ahne,Vivek Khetan,Xavier Tannier,Imbesat Hassan Rizvi,T. Czernichow,Francisco Orchard,Charline Bour,Andy E. Fano,G. Fagherazzi +8 more
TL;DR: A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multiword cause, and the corresponding effect, as expressed in diabetes-related tweets leveraging BERT-based architectures and visualized as cause-effect network.