Kuntal Kumar Pal

Kuntal Kumar Pal

AI/ML Senior Associate, CCB, JP Morgan Chase

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

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achievements

activities

affiliation

portfolio

projects

publications

Preprocessing for image classification by convolutional neural networks

Published in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2016

[PDF] [Code]

Recommended citation: K. K. Pal and K. S. Sudeep, 'Preprocessing for image classification by convolutional neural networks', 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, 2016, pp. 1778-1781. doi: 10.1109/RTEICT.2016.7808140 https://ieeexplore.ieee.org/document/7808140

Predicting Facebook-Users Personality based on Status and Linguistic Features via Flexible Regression Analysis Techniques

Published in SAC 18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Page - 339-345, 2018

[PDF] [Code]

Recommended citation: Prantik Howlader, Kuntal Kumar Pal, Alfredo Cuzzocrea, and S. D. Madhu Kumar. 2018. Predicting facebook-users' personality based on status and linguistic features via flexible regression analysis techniques. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC '18). ACM, New York, NY, USA, 339-345. DOI: https://doi.org/10.1145/3167132.3167166 http://doi.acm.org/10.1145/3167132.3167166

Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering

Published in ACM Transactions on Computing for Healthcare, 2021

[PDF] [Code \& Data]

Recommended citation: @article{10.1145/3465221, author = {Banerjee, Pratyay and Pal, Kuntal Kumar and Devarakonda, Murthy and Baral, Chitta}, title = {Biomedical Named Entity Recognition via Knowledge Guidance and Question Answering}, year = {2021}, issue_date = {October 2021}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {2}, number = {4}, issn = {2691-1957}, url = {https://doi.org/10.1145/3465221}, doi = {10.1145/3465221}, abstract = {In this work, we formulated the named entity recognition (NER) task as a multi-answer knowledge guided question-answer task (KGQA) and showed that the knowledge guidance helps to achieve state-of-the-art results for 11 of 18 biomedical NER datasets. We prepended five different knowledge contexts—entity types, questions, definitions, and examples—to the input text and trained and tested BERT-based neural models on such input sequences from a combined dataset of the 18 different datasets. This novel formulation of the task (a) improved named entity recognition and illustrated the impact of different knowledge contexts, (b) reduced system confusion by limiting prediction to a single entity-class for each input token (i.e., B, I, O only) compared to multiple entity-classes in traditional NER (i.e., Bentity1, Bentity2, Ientity1, I, O), (c) made detection of nested entities easier, and (d) enabled the models to jointly learn NER-specific features from a large number of datasets. We performed extensive experiments of this KGQA formulation on the biomedical datasets, and through the experiments, we showed when knowledge improved named entity recognition. We analyzed the effect of the task formulation, the impact of the different knowledge contexts, the multi-task aspect of the generic format, and the generalization ability of KGQA. We also probed the model to better understand the key contributors for these improvements.}, journal = {ACM Trans. Comput. Healthcare}, month = jul, articleno = {33}, numpages = {24}, keywords = {BIO tagging, multitask training, Named entity recognition, transfer learning, biomedical, NER, question answering, text tagging, BERT-CNN} } https://dl.acm.org/doi/abs/10.1145/3465221

Constructing Flow Graphs from Procedural Cybersecurity Texts

Published in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021

[PDF] [Code \& Data]

Recommended citation: @article{pal-etal-2021-constructing, title = "Constructing Flow Graphs from Procedural Cybersecurity Texts", author = "Pal, Kuntal Kumar and Kashihara, Kazuaki and Banerjee, Pratyay and Mishra, Swaroop and Wang, Ruoyu and Baral, Chitta", booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-acl.345", doi = "10.18653/v1/2021.findings-acl.345", pages = "3945--3957", } https://aclanthology.org/2021.findings-acl.345.pdf

Commonsense reasoning with implicit knowledge in natural language

Published in AKBC-2021, 2021

[Paper] – [[CodeData]](https://github.com/ari9dam/McQueen) – [Video]

Recommended citation: @inproceedings{banerjee2021commonsense, title={Commonsense Reasoning with Implicit Knowledge in Natural Language}, author={Banerjee, Pratyay and Mishra, Swaroop and Pal, Kuntal Kumar and Mitra, Arindam and Baral, Chitta}, booktitle={3rd Conference on Automated Knowledge Base Construction}, year={2021} } https://openreview.net/pdf?id=a4-fFL7aCi0

Published in , 1900

talks

teaching

Graduate Teaching Assistant

Database Management Systems, National Institute of Technology Calicut, Computer Science, 2015

  • Development of lab assignments for undergraduate students.
  • Guiding and assisting students to complete their assignments.
  • Evaluation of bi-weekly assignments.
  • Served as a member of evaluation panel for the final examination.

Graduate Teaching Assistant

Data-Structures and Algorithms, National Institute of Technology Calicut, Computer Science, 2016

  • Development of lab assignments for undergraduate students of Database laboratory.
  • Guiding and assisting students to complete their assignments.
  • Evaluation of bi-weekly assignments.
  • Served as a member of evaluation panel for the final examination.

Graduate Teaching Assistant

Undergraduate course, Arizona State University, Computer Science, 2018

  • Teaching basic concepts of C++ in 3 sessions per week.
  • Reviewing the assignments patterns and questions.
  • Guiding and assisting students to complete their assignments.
  • Holding office hours(2 hrs/week) to clarify doubts of the students.
  • Evaluation and grading of laboratory assignments.
  • Proctoring the midterms and final examinations.

Graduate Teaching Assistant

Undergraduate Online Edx course, Arizona State University, Computer Science, 2018

  • Assisting students online in understanding the basic concepts and practices of programming through Python.
  • Clarification of doubts through discussion forum.
  • Guiding students to complete their assignments.
  • Evaluation and grading of online assignments with feedback pointing out their mistakes and how they can improve.

Graduate Teaching Assistant

Graduate course, Arizona State University, Computer Science, 2019

  • Guided a group of 3 graduate student in their project on dataset OpenBookQA from AllenAI
  • Teaching basic concepts of embeddings and using BERT for Question Answering in their projects
  • Grading of project and final examination

Graduate Teaching Assistant

Graduate course, Arizona State University, Computer Science, 2019

  • Mentored 9 Teams of 5 students each in course project
    • Biomedical QA (Clicr Dataset)
    • Commonsense Reasoning QA (CosmosQA, commonsenseQA datasets)
    • Physical Commonsense Reasoning QA (PIQA Dataset)
    • Natural Language Inference (Dialogue NLI Dataset)
    • Science Question Answering (ARC Dataset)
    • Extractive Question Answering (Natural Question Dataset)
  • Teaching basic concepts of embeddings and using BERT for Question Answering in their projects
  • Grading of project and final examination

Graduate Teaching Assistant

Graduate course, Arizona State University, Computer Science, 2019

  • Currently Mentoring 6 groups of 5 students each in Course projects
    • Math Word Problem NLP
    • Numerical Reasoning
    • Semantic Textual Similarity/Paraphrasing
    • Improving Readability of Decompiled binaries using NLP
  • Teaching basic concepts of embeddings and using BERT for Question Answering in their projects
  • Grading of project and final examination

Course Project Mentor

Graduate course, Arizona State University, Computer Science, 2021

  • Mentored 4 groups of 5 students each in Course projects
    • Out-of-Domain Generalization of Numerical Reasoning Tasks
      • Robustness of T5 models in Addition and Subtraction operations in Natural Language Text.
      • Robustness with Decoding (conversion from word form to number form).
      • Robustness finding maximum and minimal number from a numerical series.
      • Robustness with sorting a numerical series in ascending and descending form
    • Methods
      • Numerical Masking during Pretraining
      • Experiments with different numerical representations (e-based, 10-based, etc)
      • Experiments with delimiters
  • Teaching basic concepts of embeddings and using BERT for Question Answering in their projects
  • Grading of project and final examination