Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …

We introduce SpERT, an attention model for span-based joint entity and relation extraction.
Our key contribution is a light-weight reasoning on BERT embeddings, which features entity …

Empowered by the advanced cognitive computing, industrial Internet-of-Things, and data
analytics techniques, today's smart manufacturing systems are ever-increasingly equipped …

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a
better understanding of the human language for linguistic-based human-computer …

Recent advances in language representation using neural networks have made it viable to
transfer the learned internal states of a trained model to downstream natural language …

Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …

This paper is a survey and an analysis of different ways of using deep learning (deep
artificial neural networks) to generate musical content. We propose a methodology based on …