K
Kishlay Jha
Researcher at University of Virginia
Publications - 24
Citations - 930
Kishlay Jha is an academic researcher from University of Virginia. The author has contributed to research in topics: Biomedical text mining & Context (language use). The author has an hindex of 9, co-authored 22 publications receiving 466 citations. Previous affiliations of Kishlay Jha include North Dakota State University & University at Buffalo.
Papers
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Proceedings ArticleDOI
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
TL;DR: An end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events, is proposed.
Journal ArticleDOI
A survey on literature based discovery approaches in biomedical domain
TL;DR: This survey attempts to consolidate and present the evolution of techniques in literature Based Discovery, and introduces the various methodologies currently employed and also the challenges yet to be tackled.
Journal ArticleDOI
MeSHProbeNet: a self-attentive probe net for MeSH indexing.
TL;DR: An end-to-end framework, MeSHProbeNet (formerly named as xgx), which utilizes deep learning and self-attentive MeSH probes to index MeSH terms, and achieves the highest scores in all the F-measures.
Proceedings ArticleDOI
Correlation Networks for Extreme Multi-label Text Classification
TL;DR: The Correlation Networks (CorNet) architecture for the extreme multi-label text classification (XMTC) task, where the objective is to tag an input text sequence with the most relevant subset of labels from an extremely large label set, is developed.
Proceedings ArticleDOI
Generating Medical Hypotheses Based on Evolutionary Medical Concepts
TL;DR: A novel dynamic Medical Subject Heading embedding model is proposed which is able to model the evolutionary behavior of medical concepts to uncover latent associations between them and demonstrates that leveraging the evolutionary features of MeSH concepts is an effective way for predicting novel associations.