Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

In the recent past, more than 5 years or so, DL especially the large language models (LLMs)
has generated extensive studies out of a distinctly average downturn field of knowledge …

For most deep learning practitioners, sequence modeling is synonymous with recurrent
networks. Yet recent results indicate that convolutional architectures can outperform …

Deep learning methods employ multiple processing layers to learn hierarchical
representations of data, and have produced state-of-the-art results in many domains …

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

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

Continuous word representations, trained on large unlabeled corpora are useful for many
natural language processing tasks. Popular models that learn such representations ignore …

State-of-the-art sequence labeling systems traditionally require large amounts of task-
specific knowledge in the form of hand-crafted features and data pre-processing. In this …

This article offers an empirical exploration on the use of character-level convolutional
networks (ConvNets) for text classification. We constructed several large-scale datasets to …

Target-based sentiment analysis involves opinion target extraction and target sentiment
classification. However, most of the existing works usually studied one of these two sub …