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Found 14 papers, 1 papers with code
Analysis of Resource-efficient Predictive Models for Natural Language Processing
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.
CLPLM: Character Level Pretrained Language Model for ExtractingSupport Phrases for Sentiment Labels
In this paper, we have designed a character-level pre-trained language model for extracting support phrases from tweets based on the sentiment label.
CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter
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.
Looking for COVID-19 misinformation in multilingual social media texts
CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts.
COVID-19 Misinformation on Twitter: Multilingual Analysis
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.
Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets
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.
Improving Neural Text Summarization using Knowledge Graphs
In this paper, we propose a method for extractive text summarization using auto-regressive transformers.
CLPLM: Character Level Pretrained Language Model for Extracting Support Phrases for Sentiment Labels
In this paper, we have designed a character-level pre-trained language model for extracting support phrases from tweets based on the sentiment label.
Biomedical Network Link Prediction using Neural Network Graph Embedding
In this paper, we aim at Graph embedding learning for automatic grasping of low-dimensional node representation on biomedical networks.
M2D: A Multi-modal Framework for Automatic Medical Diagnosis
In this paper, we present M2D: a multimodal deep learning framework for automatic medical condition diagnosis via transfer learning.
Towards Automatic Online Hate Speech Intervention Generation using Pretrained Language Model
The focus is to directly intervene in the conversation with textual responses that counter the hate content and prevent it from further spreading.
QuesBELM: A BERT based Ensemble Language Model for Natural Questions
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.
Towards Automatic Sentiment-based Topic Phrase Generation
For obtaining a comprehensive understanding and knowledge of customers’ expectations and demands, analysis of user-generated online product and service reviews is very important.
MemeSem:A Multi-modal Framework for Sentimental Analysis of Meme via Transfer Learning
For our experiment, we prepared a dataset consisting of 10, 115 internet memes with three sentiment classes- (Positive, Negative and Neutral).
