What is word embedding?
Word embedding is an unsupervised method required for various Natural Language Processing (NLP) tasks like text classification, sentiment analysis, etc. Generating word embeddings from Bidirectional Encoder Representations from Transformers (BERT) is an efficient technique. BERT can be commonly referred to as a pre-trained language model, which can also be used for NLP tasks by fine-tuning.
How to Generate Word Embedding using BERT?
Word embedding is an important part of the NLP process. It is responsible to capture the semantic meaning of words, reduce dimensionality, add contextual information, and promote efficient learning by transferring linguistic knowledge via pre-trained embeddings. As a result, we get enhanced performance with limited task-specific data. In this article, we are going to understand BERT and how it’s going to generate embeddings.