Quiz on Processing Power

What does "processing power" measure?

• Storage capacity of a computer

• Speed and capacity of a computer's CPU and cores

• Internet bandwidth

• Algorithm efficiency

How does the bag-of-words algorithm represent text?

• Using vectors based on word order

• Using vectors based on synonyms

• Using contextual embeddings

• Using vectors based on word frequency

Which problem arises from BoW's inability to handle synonyms?

• Slower computations

• Enhanced sensitivity to rare words in the dataset

• inefficiency in understanding word meaning

• Reduced storage requirements

What does tokenization involve?

• Splitting text into numerical vectors

• Removing punctuation and special characters

• Splitting text into individual words

• Converting words to their base forms

What is the purpose of the pre-processing stage stemming and lemmatization?

• To remove punctuation

• To convert words into root forms

• To count word frequency

• To handle large datasets

How do word embeddings differ from BoW?

• They capture word meanings and relationships

• They use high-dimensional vectors

• They use fixed-length dense vectors

• They generate sparse matrices

Why is high processing power important for chatbots?

• To improve storage capacity

• To handle large datasets quickly and accurately

• To reduce CPU cores

• To eliminate preprocessing

What limitation does BoW have in understanding text context?

• It treats words as isolated features

• It relies on high-dimensional embeddings

• It depends on tokenization

• It uses advanced semantic models

How does processing power influence the chatbot's ability to handle multiple users simultaneously?

• By enabling the efficient storage of input data.

• By ensuring the CPU can quickly process large datasets and complex computations.

• By minimizing the need for vector representations.

• By reducing memory usage for large vocabularies.

Why is stemming an important step in preprocessing?

• It removes grammatical errors in text.

• It standardizes the order of words in a sentence

• It reduces redundancy

• It increases the vocabulary size for BoW representation.