In an era of rapidly growing multimedia data, the need for robust and efficient classification systems has become critical, specifically the identification of class names and poses or styles. This ...

Background: Free-text comments in patient-reported outcome measures (PROMs) data provide insights into health-related quality of life (HRQoL). However, these comments are typically analysed using ...

Large Language Models (LLMs) ushered in a technological revolution. We breakdown how the most important models work. byLanguage Models (dot tech)@languagemodels byLanguage Models (dot ...

when i run the python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/faster_rcnn_inception_v2_pets.config..... (tf) D:\Anaconda\envs\tf ...

Abstract: The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, ...

Are you overwhelmed by the sheer volume of text messages cluttering your Mac or iPad? You’re not alone. Many users find themselves endlessly scrolling through conversations, searching for important ...

Abstract: Multi-label text categorization is a crucial task in Natural Language Processing, where each text instance can be simultaneously assigned to numerous labels. In this research, our goal is to ...

Natural Language to SQL (NL2SQL) technology has emerged as a transformative aspect of natural language processing (NLP), enabling users to convert human language queries into Structured Query Language ...

Multi-label text classification (MLTC) assigns multiple relevant labels to a text. While deep learning models have achieved state-of-the-art results in this area, they require large amounts of labeled ...