1 code implementation • ACL 2021 • Vivek Gupta, Prerna Bharti, Pegah Nokhiz, Harish Karnick
Most earlier work on text summarization is carried out on news article datasets.
1 code implementation • 24 Apr 2021 • Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Neal Patwari, Suresh Venkatasubramanian
When it comes to studying the impacts of decision making, the research has been largely focused on examining the fairness of the decisions, the long-term effects of the decision pipelines, and utility-based perspectives considering both the decision-maker and the individuals.
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • Pranshi Yadav, Priya Yadav, Pegah Nokhiz, Vivek Gupta
User-generated contents{'} score-based prediction and item recommendation has become an inseparable part of the online recommendation systems.
1 code implementation • 18 May 2020 • Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha Talukdar
One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation.
no code implementations • ACL 2020 • Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, Vivek Srikumar
In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them.
1 code implementation • 18 Nov 2019 • Vivek Gupta, Ankit Saw, Pegah Nokhiz, Harshit Gupta, Partha Talukdar
Through extensive experiments on multiple real-world datasets, we show that SCDV-MS embeddings outperform previous state-of-the-art embeddings on multi-class and multi-label text categorization tasks.
Ranked #5 on Document Classification on Reuters-21578 (F1 metric)
no code implementations • 7 Sep 2019 • Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, Suresh Venkatasubramanian
We measure recourse as the distance of an individual from the decision boundary of a classifier.