Introduction to Word Embedding
Explore the fundamentals of word embedding and understand how textual data is converted into numerical vectors for NLP. This lesson covers frequency-based methods like TF-IDF and CountVectorizer and introduces prediction-based embeddings using Word2Vec. Gain insight into how word embeddings capture semantic relationships and context, essential for advanced NLP projects.
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What is word embedding?
If you know or have knowledge about NLP, then you might have created vectors for text, i.e., converting textual data to numbers using the two most used techniques: TF-IDF (Term Frequency-Inverse Document Frequency) and CountVectorizer. Let’s look closely at these two techniques.
TF-IDF
It stands for Term Frequency-Inverse Document Frequency. It is a statistical measure that evaluates how relevant a word is to a document in a collection of documents. This is done by multiplying two metrics: ...