B
Bhargavi Paranjape
Researcher at University of Washington
Publications - 25
Citations - 707
Bhargavi Paranjape is an academic researcher from University of Washington. The author has contributed to research in topics: Computer science & Language model. The author has an hindex of 7, co-authored 19 publications receiving 459 citations. Previous affiliations of Bhargavi Paranjape include Indian Institutes of Technology & Indian Institute of Technology Kharagpur.
Papers
More filters
Proceedings ArticleDOI
Stop clickbait: detecting and preventing clickbaits in online news media
TL;DR: Wang et al. as mentioned in this paper proposed clickbait detection and personalized blocking approaches to detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines.
Proceedings Article
ProtoNN: compressed and accurate kNN for resource-scarce devices
Chirag Gupta,Arun Sai Suggala,Ankit Goyal,Harsha Vardhan Simhadri,Bhargavi Paranjape,Ashish Kumar,Saurabh Goyal,Raghavendra Udupa,Manik Varma,Prateek Jain +9 more
TL;DR: Pro-toNN as mentioned in this paper learns a small number of prototypes to represent the entire training set, sparse low dimensional projection of data, and joint discriminative learning of the projection and prototypes with explicit model size constraint.
Posted Content
Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media
TL;DR: This work attempts to automatically detect clickbait detection and then builds a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines, and offers each reader an option to block clickbaits she doesn't want to see.
Proceedings ArticleDOI
Entity Projection via Machine Translation for Cross-Lingual NER
TL;DR: This work proposes a system that improves over prior entity-projection methods by leveraging machine translation systems twice: first for translating sentences and subsequently for translating entities; and matching entities based on orthographic and phonetic similarity; and identifying matches based on distributional statistics derived from the dataset.
Proceedings ArticleDOI
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
TL;DR: Through extensive experiments on multi-class and multi-label classification tasks, this work outperforms the previous state-of-the-art method, NTSG and achieves a significant reduction in training and prediction times compared to other representation methods.