Accepted Publications

• CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter

  [TLDR]

  Raj Ratn Pranesh, Mehrdad Farokhenajd, Ambesh Shekhar, Genoveva Vargas-Solar
  Association for Computational Linguistics (ACL), 2021
  Paper

  TLDR: This paper presents a multilingual COVID-19 re- lated tweet analysis method, CMTA, that uses BERT, a deep learning model for multilingual tweet misinformation detection and classification. CMTA extracts features from multilingual textual data, which is then categorized into specific information classes. Classification is done by a Dense-CNN model trained on tweets manually annotated into information classes (i.e., ’false’, ’partly false’, ’mislead- ing’). The paper presents an analysis of mul- tilingual tweets from February to June, show- ing the distribution type of information spread across different languages. To access the performance of the CMTA multilingual model, we performed a comparative analysis of 8 monolingual model and CMTA for the misin- formation detection task. The results show that our proposed CMTA model has surpassed various monolingual models which consolidated the fact that through transfer learning a multilingual framework could be developed.

• Looking for COVID-19 misinformation inmultilingual social media texts

  [TLDR]

  Raj Ratn Pranesh, Mehrdad Farokhenajd, Ambesh Shekhar, Genoveva Vargas-Solar
  ADBIS 2021: 25th European Conference on Advances in Databases and Information Systems
  Paper

• Biomedical Network Link Prediction using Neural Network Graph Embedding

  [TLDR]

  Sumit Kumar, Raj Ratn Pranesh, Ambesh Shekhar
  ACM CoDS-COMAD, 2021 (Young Researchers’ Symposium)
  Paper

  TLDR: In this paper, we aim at Graph embedding learning for automatic grasping of low-dimensional node representation on biomedical networks. The purpose is to use different neural Graph embedding methods for conducting analysis on 3 major biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) classification, and protein-protein interaction (PPI) classification. We observe that graph embedding method achieve a promising result without the use of any biological features.

• A Conglomerate of Multiple OCR Table Detection and Extraction

  [TLDR]

  Smita Pallavi, Raj Ratn Pranesh, Sumit Kumar
  22nd International Conference on Document Analysis and Recognition, 2020
  Paper

  TLDR: Information representation as tables are compact and concise method that eases searching, indexing, and storage requirements. Extracting and cloning tables from parsable documents is easier and widely used, however industry still faces challenge in detecting and extracting tables from OCR documents or images. This paper proposes an algorithm that detects and extracts multiple tables from OCR document. The algorithm uses a combination of image processing techniques, text recognition and procedural coding to identify distinct tables in same image and map the text to appropriate corresponding cell in dataframe which can be stored as Comma-separated values, Database, Excel and multiple other usable formats.

• Automated Medical Assistance: Attention Based Consultation System

  [TLDR]

  Raj Ratn Pranesh, Ambesh Shekhar, Sumit Kumar
  NeurIPS, 2020 MLPH: Machine Learning in Public Health Workshop) 2020
  Paper

  TLDR: We designed three transformers based encoder-decoder model, namely, BERT, GPT2, and BART and trained them on large the dialogue dataset for text generation. We performed a comparative study of the models and in our analysis, we found that the BART model generates a doctor-like response and contains clinically informative data. The overall generated results were very promising and show that through transfer learning pre-trained transformers are reliable for developing automated medical assistance system and doctor-like-treatments.

• MemeSem:A Multi-modal Framework for Sentimental Analysis of Meme via Transfer Learning

  [TLDR]

  Raj Ratn Pranesh, Ambesh Shekhar
  37th International Conference on Machine Learning (ICML), 2020 (4th Lifelong Learning Workshop)
  Paper

  TLDR: In this paper, we present MemeSem- a multimodal deep neural network framework for sentiment analysis of memes via transfer learning. Our proposed model utilizes VGG19 pretrained on ImageNet dataset and BERT language model to learn the visual and textual feature of the meme and combine them together to make predictions.

• QuesBELM: A BERT based Ensemble Language Model for Natural Questions

  [TLDR]

  Raj Ratn Pranesh, Ambesh Shekhar, Smita Pallavi
  5th IEEE ICCCS (International Conference)
  Paper

  TLDR: In our work, we systematically compare the performance of powerful variant models of Transformer architectures- ’BERTbase, BERT-large-WWM and ALBERT-XXL’ over Natural Questions dataset. We also propose a state-of-the-art BERT based ensemble language model- QuesBELM. QuesBELM leverages the power of existing BERT variants combined together to build a more accurate stacking ensemble model for question answering (QA) system

• Analysis of Resource-efficient Predictive Models for Natural Language Processing

  [TLDR]

  Raj Ratn Pranesh, Ambesh Shekhar
  EMNLP, 2020 (SustaiNLP Workshop)
  Paper

  TLDR: In this paper, we presented an analyses of the resource efficient predictive models, namely Bonsai, Binary Neighbor Compression(BNC), ProtoNN, Random Forest, Naive Bayes and Support vector machine(SVM), in the machine learning field for resource constraint devices. These models try to minimize resource requirements like RAM and storage without hurting the accuracy much. We utilized these models on multiple benchmark natural language processing tasks, which were sentimental analysis, spam message detection, emotion analysis and fake news classification.

• Classifying Micro-text Document Datasets: Application to Query Expansion of Crisis-Related Tweets

  [TLDR]

  Mehrdad Farokhen, Raj Ratn Pranesh, Javier A. Espinosa-Oviedo
  ICSOC, 2020 (STRAPS 2020 Workshop).
  Paper

  TLDR: This paper introduces an approach based on classification and query expansion techniques in the context of micro-texts (i.e., tweets) search. In our approach, a user’s query is rewritten using a classified vocabulary derived from top-k results, to reflect her search intent better.

• A Fine-Grained Analysis of Misinformation in COVID-19 Tweets

  [TLDR]

  Sumit Kumar, Raj Ratn Pranesh, Kathleen M. Carle
  Computational and Mathematical Organization Theory, Springer.
  Paper

  TLDR: In this paper, We have proposed a Twitter dataset for fine-grained classification. Our dataset is consist of 1970 manually annotated tweets and is categorized into 4 misinformation classes, i.e, “Irrelevant”, “Conspiracy”, “True Information”, and “False Information” on the basis of response erupted during COVID-19. In this work, we also generated useful insights on our dataset and performed a systematic analysis of various language models.

• CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels

  [TLDR]

  Raj Ratn Pranesh, Sumit Kumar, Ambesh Shekhar
  17th International Conference on Natural Language Processing, 2020.
  Paper

  TLDR: In this paper, we have designed a character-level pre-trained language model for extracting support phrases from tweets based on the sentiment label. We also propose a character-level ensemble model designed by properly blending Pre-trained Contextual Embeddings (PCE) models- RoBERTa, BERT, and ALBERT along with Neural network models-RNN, CNN and WaveNet at different stages of the model. For a given tweet and associated sentiment label, our model predicts the span of phrases in a tweet that prompts the particular sentiment in the tweet.