X-Academy (Post Test 4): Text Mining and Applications in HR Case

How does Machine Learning contribute to Text Mining?

• By improving hardware efficiency

• Using algorithms to uncover patterns in text

• Focusing on financial modeling

• By reducing the need for software

Why is feature engineering crucial in Text Mining?

• To simplify text

• To transform text into numerical feature vectors

• To make text more colorful

• To reduce the amount of text

What is a significant challenge in semantic analysis in Text Mining?

• Mining meaningful relationships from text

• Changing the font of the text

• Making the text shorter

• Ignoring the content of the text

What does the Bag of Words model do in Text Mining?

• Reduces the size of the text

• Translates text into different languages

• Focuses on the grammar of the text

• Represents text disregarding grammar but keeping multiplicity

What does Hugging Face primarily contribute to Text Mining?

• Physical robot models

• Financial advice

• Open-sourcing transformer models like BERT

• Legal consulting

What does GPT stand for in the field of AI?

• General Processing Technology

• Generalized Prompt Training

• Generative Pre-trained Transformer

• Graphical Processing Tool

What is Prompt Engineering in the context of LLMs like GPT?

• Designing physical hardware for computers

• Crafting inputs to effectively guide the model’s response

• Manual data entry technique

• A method for financial analysis

What advantage does the transformer architecture offer in models like GPT and BERT?

• Focuses on hardware improvements

• Enhances financial data processing

• Used for improving manual labor efficiency

• Enables understanding of context in language processing

What is the primary function of an encoder in a Transformer model?

• It converts text into a numerical format for hardware processing.

• It processes the input data to create a contextualized representation.

• It is used to manually encode language rules into the model.

• It decodes the output into human-readable text.

What is the role of a decoder in a Transformer-based model?

• To physically modify the input data for better processing.

• To encode input data into a machine-readable format.

• To generate the final output based on the encoder’s representations and its own processing.

• To manually decode complex algorithms for basic tasks.