Natural language processing is important because it helps solve ambiguities in the language and adds a useful numerical structure to data for many end-to-end applications. Many machine learning algorithms and nearly all deep learning architectures are unable to process strings or plain text in their initial form. It requires numbers as input to perform any kind of job, such as classification, regression, etc. in general terms. With the vast amount of data contained in text format, it is imperative to extract knowledge from it and build applications.
In this research, we explain the stages of text processing down to methods of text embedding that represents a modern way of representing words as digital vectors to be processed by machine learning algorithms, as it introduces the field and a quick overview of deep learning structures such as Embeddings from Language Models (ELMO) Model.
Al-Baath University Journal.
2021.
A review of the most important Word Embedding Techniques in Natural Language Processing
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