Chatbots for Beginners: A Complete Guide to Build Chatbots - Machine Learning-Based Chatbots: Word Tokens and Remove ASC

The video tutorial explains the process of normalizing text by defining a function called 'normalize'. It covers removing punctuation using a dictionary, tokenizing words with NLTK, and removing ASCII values through encoding and decoding. The tutorial concludes with a brief overview of the steps and hints at the next video focusing on removing tags.

What is the primary input for the normalize function?

• A text string

• A list of numbers

• A JSON object

• A dictionary

How is punctuation removed from the text?

• By converting text to uppercase

• By manually deleting each punctuation mark

• By using a punctuation dictionary

• By using a regular expression

Which module is used for word tokenization?

• Pandas

• NLTK

• Scikit-learn

• NumPy

What is the purpose of translating text after tokenization?

• To change the language

• To convert text to uppercase

• To remove punctuation

• To enhance readability

What is the first step in removing ASCII numbers?

• Developing a new dictionary of words

• Creating a list of ASCII values

• Converting text to binary

• Using a regular expression

How are ASCII values handled during encoding?

• They are replaced with symbols

• They are ignored

• They are converted to UTF-16

• They are highlighted

What is the common format used for decoding text?

• UTF-16

• UTF-8

• ASCII

• Binary