The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …

Word embeddings are fundamentally a form of word representation that links the human
understanding of knowledge meaningfully to the understanding of a machine. The …

The representational basis for downstream natural language processing tasks is word
embeddings, which capture lexical semantics in numerical form to handle the abstract …

Today, extreme amounts of data are produced, and this is commonly referred to as Big Data.
A significant amount of big data is composed of textual data, and as such, text processing …

The basis of applying deep learning to solve natural language processing tasks is to obtain
high-quality distributed representations of words, ie, word embeddings, from large amounts …

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 …

Word embedding, where semantic and syntactic features are captured from unlabeled text
data, is a basic procedure in Natural Language Processing (NLP). The extracted features …

Text data has been growing drastically in the present day because of digitalization. The
Internet, being flooded with millions of documents every day, makes the task of text …

Efficient word representation techniques (word embeddings) with modern machine learning
models have shown reasonable improvement on automatic text classification tasks …

A word embedding is a low-dimensional, dense and real-valued vector representation of a
word. Word embeddings have been used in many NLP tasks. They are usually generated …