Search Results for author: Nitin Ramrakhiyani

Found 12 papers, 0 papers with code

Extracting Events from Industrial Incident Reports

no code implementations ACL (CASE) 2021 Nitin Ramrakhiyani, Swapnil Hingmire, Sangameshwar Patil, Alok Kumar, Girish Palshikar

Incidents in industries have huge social and political impact and minimizing the consequent damage has been a high priority.

Transfer Learning

Extraction of Message Sequence Charts from Software Use-Case Descriptions

no code implementations NAACL 2019 Girish Palshikar, Nitin Ramrakhiyani, Sangameshwar Patil, Sachin Pawar, Swapnil Hingmire, Vasudeva Varma, Pushpak Bhattacharyya

We apply this tool to extract MSCs from several real-life software use-case descriptions and show that it performs better than the existing techniques.

Extraction of Message Sequence Charts from Narrative History Text

no code implementations WS 2019 Girish Palshikar, Sachin Pawar, Sangameshwar Patil, Swapnil Hingmire, Nitin Ramrakhiyani, Harsimran Bedi, Pushpak Bhattacharyya, Vasudeva Varma

In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering.

Dependency Parsing

Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals

no code implementations4 Dec 2017 Girish Keshav Palshikar, Sachin Pawar, Saheb Chourasia, Nitin Ramrakhiyani

All techniques are illustrated using a real-life dataset of supervisor assessment and peer feedback text produced during the PA of 4528 employees in a large multi-national IT company.

Clustering General Classification +4

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction

no code implementations6 Sep 2017 Shashank Gupta, Sachin Pawar, Nitin Ramrakhiyani, Girish Palshikar, Vasudeva Varma

Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem.

Measuring Topic Coherence through Optimal Word Buckets

no code implementations EACL 2017 Nitin Ramrakhiyani, Sachin Pawar, Swapnil Hingmire, Girish Palshikar

Measuring topic quality is essential for scoring the learned topics and their subsequent use in Information Retrieval and Text classification.

General Classification Information Retrieval +4

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