tokenization and waord embedding.txt

Word embedding and tokenization are both important techniques in natural language 
processing (NLP).

Tokenization is the process of breaking a text into individual tokens or words. This is 
a fundamental step in NLP because it allows us to analyze text at the word level, 
rather than treating it as a long string of characters. There are several methods for
tokenization, including whitespace tokenization, which splits the text on spaces, and 
word-based tokenization, which splits the text on words.

Word embedding, on the other hand, is a technique used to represent words as dense 
vectors of real numbers, which can be used as inputs to machine learning models. The 
idea behind word embedding is to capture the meaning of a word based on its context 
within a corpus of text. This is typically done using neural network models such as 
Word2Vec or GloVe.

In summary, tokenization is the process of breaking text into individual words or 
tokens, while word embedding is a technique for representing words as dense vectors 
that capture their meaning based on their context in a corpus of text.