Progress in natural language processing has been dictated by the rule of more: more data,
more computing power, more complexity, best exemplified by deep learning Transformers …

This article brings two major contributions. First, we present the results of a critical analysis
of recent scientific articles about neural and non-neural approaches and representations for …

With the advent of the modern pre-trained Transformers, the text preprocessing has started
to be neglected and not specifically addressed in recent NLP literature. However, both from …

Transformer-based deep learning is currently the state-of-the-art in many NLP and IR tasks.
However, fine-tuning such Transformers for specific tasks, especially in scenarios of ever …

With the presence of a massive amount of digitally recorded data, an automated
computation can be preferable over the manual approach to evaluate sentiments within …

The literature has not fully and adequately explained why contextual (eg, BERT-based)
representations are so successful to improve the effectiveness of some Natural Language …

Public feelings and reactions associated with finance are gaining significant importance as
they help individuals, public health, financial and non-financial institutions, and the …

The sampling-based approximation method has demonstrated its potential in various
domains such as machine learning, query processing, and data analysis. Most preceding …

Code retrieval consists of finding relevant code snippets regarding a programmer's query—
an increasingly important task due to software ubiquity. Although significant progress has …

Fine-tuning transformer-based deep-learning models are currently at the forefront of natural
language processing (NLP) and information retrieval (IR) tasks. However, fine-tuning these …