Despite that pre-trained word embedding models have advanced a wide range of natural
language processing applications, they ignore the contextual information and meaning …

This paper proposes an intelligent text classification framework for a resource-constrained
language like Bengali, which is considered a challenging task due to the lack of standard …

Abstract Recently, Word Embeddings have been introduced as a major breakthrough in
Natural Language Processing (NLP) to learn viable representation of linguistic items based …

In recent years, the amount of digital text contents or documents in the Bengali language has
increased enormously on online platforms due to the effortless access of the Internet via …

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 …

Abstract Language model pre-training has proven to be useful in learning universal
language representations. As a state-of-the-art language model pre-training model, BERT …

Due to the expansion of data generation, more and more natural language processing
(NLP) tasks are needing to be solved. For this, word representation plays a vital role …

Word embeddings, which represent words as numerical vectors in a high-dimensional
space, are contextualized by generating a unique vector representation for each sense of a …

Word embeddings act as an important component of deep models for providing input
features in downstream language tasks, such as sequence labelling and text classification …

This article studies convolutional neural networks for Tigrinya (also referred to as Tigrigna),
which is a family of Semitic languages spoken in Eritrea and northern Ethiopia. Tigrinya is a …