What is the primary goal of Natural Language Processing (NLP)?

• To create new human languages

• To enable computers to understand and interact with human language

• To replace human communication entirely

• To encrypt text data

Which of the following is NOT a common NLP application?

• Text summarization

• Sentiment analysis

• Image recognition

• Named entity recognition

In NLP, what does the term "tokenization" refer to?

• Encrypting text data

• Breaking down text into smaller units, typically words

• Combining words into sentences

• Translating text from one language to another

What is the main difference between stemming and lemmatization?

• Stemming is faster but less accurate, while lemmatization is slower but more accurate

• Stemming works only for nouns, while lemmatization works for all parts of speech

• Stemming adds suffixes, while lemmatization removes them

• There is no difference; they are two terms for the same process

What does TF-IDF stand for in the context of NLP?

• Text Formatting-Indirect Document Filtering

• Total Findings-Inferred Data Formation

• Text Function-Integrated Document Format

• Term Frequency-Inverse Document Frequency

Which NLP approach relies on pre-existing lexical resources like dictionaries and thesauri?

• Corpus-based approach

• Statistical approach

• Dictionary-based approach

• Deep learning approach

What is the main limitation of the Bag of Words (BoW) model?

• It requires too much computational power

• It can only be used for short texts

• It disregards word order and context

• It only works for English language texts

Which of the following is a characteristic of word embeddings like Word2Vec?

• They produce sparse, high-dimensional vectors

• They generate different representations for a word based on its context

• They map semantically similar words to nearby points in a vector space

• They require manually labeled training data

What is a key advantage of contextual embeddings (like BERT or GPT) over traditional word embeddings?

• They are computationally less intensive

• They can handle out-of-vocabulary words better

• They generate different representations for a word based on its context

• They require less training data

In the context of NLP, what does POS tagging refer to?

• Point of Sale tagging

• Probability of Sequence tagging

• Part of Speech tagging

• Parsing of Sentences tagging