Motivation: The scientific literature embeds an enormous amount of relational knowledge,
encompassing interactions between biomedical entities, like proteins, drugs, and symptoms …

The latest batch of research has equipped language models with the ability to attend over
relevant and factual information from non-parametric external sources, drawing a …

Text document classification approaches are designed to categorise documents into
predefined classes. These approaches have two main components: document …

Text classification is a central task in Natural Language Processing (NLP) that aims at
categorizing text documents into predefined classes or categories. It requires appropriate …

Micro-blogging social site Twitter has emerged as a rich source of unstructured text
information which could be processed and analysed to extract people's opinions about …

Aspect-based sentiment analysis (ABSA) has recently attracted increasing attention due to
its extensive applications. Most of the existing ABSA methods been applied on small-sized …

In knowledge graph representation learning, link prediction is among the most popular and
influential tasks. Its surge in popularity has resulted in a panoply of orthogonal embedding …

Preventing organizations from Cyber exploits needs timely intelligence about Cyber
vulnerabilities and attacks, referred as threats. Cyber threat intelligence can be extracted …

In this paper, we address the problem of multi-modal retrieval of fashion products. State-of-
the-art (SOTA) works proposed in literature use vision-and-language transformers to assign …

The automatic extraction of biomedical events from the scientific literature has drawn keen
interest in the last several years, recognizing complex and semantically rich graphical …