Limitations of classical methods in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a major limitation of classical machine learning methods when handling large and complex datasets?

Classical methods often struggle with scalability and may not perform well on very large or complex datasets because they rely on manual feature engineering and simpler models.

beginner

Why do classical methods require manual feature engineering?

Classical methods depend on human experts to select and design features that represent the data well, as they cannot automatically learn complex patterns from raw data.

intermediate

How do classical methods perform on unstructured data like images or raw text?

They usually perform poorly because classical methods need structured, numerical input and cannot easily extract meaningful features from unstructured data without extensive preprocessing.

intermediate

What is a common problem of classical methods related to model flexibility?

Classical methods often have limited model flexibility, meaning they cannot capture very complex relationships or patterns in data compared to modern deep learning models.

intermediate

Why can classical methods be less effective for tasks requiring context understanding?

Because classical methods do not model context or sequence information well, they struggle with tasks like natural language understanding where context is important.

What is a key reason classical methods need manual feature engineering?

A They have unlimited computational power

B They cannot automatically learn features from raw data

C They always use deep neural networks

D They do not require any data preprocessing

Classical methods rely on human-designed features because they lack the ability to learn complex features automatically.

Which type of data do classical methods struggle with the most?

A Unstructured data like images and raw text

B Structured numerical data

C Small datasets

D Tabular data with clear labels

Classical methods have difficulty processing unstructured data because they require structured inputs.

Why are classical methods less flexible than modern deep learning models?

A They require GPUs

B They always overfit

C They do not use any features

D They use fixed, simpler models

Classical methods often use simpler models that cannot capture complex patterns as well as deep learning.

What is a common challenge when using classical methods for natural language processing?

A They easily understand context

B They perform best on raw text

C They struggle to model sequence and context

D They require no preprocessing

Classical methods do not handle sequence or context well, which is important in language tasks.

Which of the following is NOT a limitation of classical methods?

A Automatic feature learning from raw data

B Poor scalability on large datasets

C Need for manual feature engineering

D Limited ability to model complex patterns

Automatic feature learning is a strength of modern methods, not classical ones.

Explain the main limitations of classical machine learning methods compared to modern approaches.

Think about what classical methods need from humans and what they struggle to do automatically.

Describe why classical methods are less effective for natural language processing tasks.

Consider how language data is different from simple numbers and how classical methods handle data.