N
Nitin Ramrakhiyani
Researcher at Tata Consultancy Services
Publications - 32
Citations - 160
Nitin Ramrakhiyani is an academic researcher from Tata Consultancy Services. The author has contributed to research in topics: Performance appraisal & Task (project management). The author has an hindex of 6, co-authored 29 publications receiving 108 citations. Previous affiliations of Nitin Ramrakhiyani include International Institute of Information Technology & Tata Research Development and Design Centre.
Papers
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Journal ArticleDOI
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction mention extraction
Shashank Gupta,Sachin Pawar,Nitin Ramrakhiyani,Nitin Ramrakhiyani,Girish Keshav Palshikar,Vasudeva Varma +5 more
TL;DR: In this paper, a semi-supervised learning based RNN model was proposed to leverage unlabeled data also present in abundance on social media, which outperformed fully supervised learning based baseline which relies on large annotated corpus for a good performance.
Book ChapterDOI
Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets
Shashank Gupta,Manish Gupta,Vasudeva Varma,Sachin Pawar,Nitin Ramrakhiyani,Girish Keshav Palshikar +5 more
TL;DR: In this paper, a semi-supervised method based on co-training was proposed to exploit a large pool of unlabeled tweets to augment the limited supervised training data, and as a result enhance the performance.
Journal ArticleDOI
Approaches to Temporal Expression Recognition in Hindi
TL;DR: In this work, different approaches for identification and classification of temporal expressions in Hindi are developed and analyzed, and a reusable gold standard dataset for temporal tagging in Hindi is developed.
Book ChapterDOI
Multi-task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets
Shashank Gupta,Manish Gupta,Vasudeva Varma,Sachin Pawar,Nitin Ramrakhiyani,Girish Keshav Palshikar +5 more
TL;DR: A novel joint multi-task learning method to automatically generate weak supervision dataset for the auxiliary task when a large pool of unlabeled tweets is available and outperforms the state-of-the-art methods for the ADR mention extraction task by 7.2% in terms of F1 score.
Posted Content
Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction
Shashank Gupta,Sachin Pawar,Nitin Ramrakhiyani,Nitin Ramrakhiyani,Girish Keshav Palshikar,Vasudeva Varma +5 more
TL;DR: This study studies the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media and proposes a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media.