no code implementations • EMNLP (sustainlp) 2020 • Raj Pranesh, Ambesh Shekhar
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.
no code implementations • ICON 2020 • Raj Pranesh, Sumit Kumar, Ambesh Shekhar
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 • ACL 2021 • Raj Pranesh, Mehrdad Farokhenajd, Ambesh Shekhar, Genoveva Vargas-Solar
To access the performance of the CMTA multilingual model, we performed a comparative analysis of 8 monolingual model and CMTA for the misinformation detection task.
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 • 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, 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 • 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.
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).