Tokenization

The video tutorial explains tokenization and count vectorizer, key techniques in text processing. Tokenization involves splitting text into tokens, which are small units with semantic value. An example using movie reviews illustrates this process. Count vectorizer then converts these tokens into a sparse matrix, enabling text to be transformed into numeric form for machine learning. The tutorial concludes with an application of these techniques in classification tasks, highlighting the efficiency of linear models.

What is the primary purpose of tokenization in text processing?

• To merge multiple strings into one

• To split a string into smaller units called tokens

• To convert text into binary code

• To encrypt text for security

How many tokens are generated from the first movie review example?

• Three tokens

• Five tokens

• Six tokens

• Four tokens

What does the count vectorizer do with a collection of text documents?

• It summarizes them into a single paragraph

• It encrypts them for secure storage

• It converts them into a matrix of token counts

• It translates them into different languages

Why are linear models often sufficient for classification in this context?

• Because they are the most complex models available

• Because most features for a data point will be zero

• Because they are the only models that can handle text data

• Because they require less computational power

What is the benefit of transforming text into its numeric form for machine learning?

• It makes the text more readable

• It reduces the size of the data

• It allows for easier storage of data

• It enables the use of machine learning algorithms for analysis