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Frequently Asked Questions
Why do we use word embeddings in NLP?
Word embedding in NLP allows you to extract features out of the text with which you can utilize them into a machine learning model for text data. It also visualises the pattern lying beneath the corpus usage that was initially used to train them.
Is word embedding supervised or unsupervised?
Word embedding is an unsupervised process that finds great usage in text analysis tasks such as text classification, machine translation, entity recognition, and others.
What is word vectorization?
Word vectorization is an NLP process that converts individual words into vectors and enables words with the same meaning to have the same representation. It allows the text to be analyzed and consumed by the machine learning models smoothly.
Which word embedding is the best?
The best word embedding techniques are GloVe and Word2Vec.
Why is embedding important?
Word embedding in NLP is an important aspect that connects a human language to that of a machine. You can reuse it across models while solving most natural language processing problems.
What are the applications of Word Embedding?
Word embedding finds applications in analyzing survey responses, verbatim comments, music/video recommendation systems, retrofitting, and others.