*3.3. Embedding*

We assign a vector value for each token in a sentence, e.g., based on the order they appeared in our corpus, and we create a vector of numerical values. The mapping of word tokens to numerical values of vectors is referred to as embedding. There are various ways of creating word embeddings. Term Frequency-Inverse Document Frequency Tokenization creates a matrix of TF-IDF features which are used to create the embedding. Every sentence is converted to a single dimension vector of numerical elements regardless of the tokenization method. To address variable sentence length, we define a large vector length and we fill the numerical vector with zeroes, a process known as padding. Most common and effective word embedding methods are created based on term co-occurrence throughout a large corpus [**? ?** ].
