no code implementations • 27 Aug 2021 • Sumit Kumar, Raj Ratn Pranesh
Our dataset comprises 9165 manually annotated tweets that target the Black Lives Matter movement.
no code implementations • 3 May 2021 • Raj Ratn Pranesh, Mehrdad Farokhnejad, Ambesh Shekhar, Genoveva Vargas-Solar
CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts.
no code implementations • 6 Jan 2021 • Raj Ratn Pranesh, Mehrdad Farokhenajd, Ambesh Shekhar, Genoveva Vargas-Solar
This paper presents a multilingual COVID-19 related tweet analysis method, CMTA, that usesBERT, 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.
no code implementations • 6 Nov 2020 • Raj Ratn Pranesh, Ambesh Shekhar, Anish Kumar
We have presented a systematic analysis of multiple intramodal as well as cross-modal fusion strategies and their effect over the performance of the multimodal disaster classification system.
no code implementations • 24 Oct 2020 • Ambesh Shekhar, Raj Ratn Pranesh, Sumit Kumar
In this paper, we propose a method for extractive text summarization using auto-regressive transformers.
no code implementations • 24 Oct 2020 • Raj Ratn Pranesh, Ambesh Shekhar, Sumit Kumar
In this paper, we have designed a character-level pre-trained language model for extracting support phrases from tweets based on the sentiment label.
no code implementations • 21 Oct 2020 • Mehrdad Farokhnejad, Raj Ratn Pranesh, Genoveva Vargas-Solar, Davoud Amiri Mehr
This paper introduces S_Covid, an end-to-end unsupervised learning based question-answering engine for exploring COVID-19 scientific literature collections.
no code implementations • 19 Oct 2020 • Raj Ratn Pranesh, Ambesh Shekhar, Sumit Kumar
In this paper, we present M2D: a multimodal deep learning framework for automatic medical condition diagnosis via transfer learning.
no code implementations • 19 Oct 2020 • Sumit Kumar, Raj Ratn Pranesh, Ambesh Shekhar
In this paper, we aim at Graph embedding learning for automatic grasping of low-dimensional node representation on biomedical networks.
no code implementations • 19 Oct 2020 • Raj Ratn Pranesh, Ambesh Shekhar, Anish Kumar
The focus is to directly intervene in the conversation with textual responses that counter the hate content and prevent it from further spreading.
no code implementations • 18 Oct 2020 • Raj Ratn Pranesh, Ambesh Shekhar, Sumit Kumar
For obtaining a comprehensive understanding and knowledge of customers’ expectations and demands, analysis of user-generated online product and service reviews is very important.
no code implementations • 18 Oct 2020 • Raj Ratn Pranesh, Ambesh Shekhar, Smita Pallavi
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.
no code implementations • 16 Oct 2020 • Smita Pallavi, Raj Ratn Pranesh, Sumit Kumar
Information representation as tables are compact and concise method that eases searching, indexing, and storage requirements.
no code implementations • 25 Jun 2020 • Sumit Kumar, Raj Ratn Pranesh, Subhash Chandra Pandey
Our dataset is consists of 9165 manually annotated tweets that target the Black Lives Matter movement.
1 code implementation • ICML Workshop LifelongML 2020 • Raj Ratn Pranesh, Ambesh Shekhar
For our experiment, we prepared a dataset consisting of 10, 115 internet memes with three sentiment classes- (Positive, Negative and Neutral).