Exploring Natural Language Processing

What is Natural Language Processing (NLP)?

• A technique for translating text from one language to another.

• A method for teaching computers to speak human languages fluently.

• Natural Language Processing (NLP) is a field of AI that enables computers to understand, interpret, and generate human language.

• A system for generating random text without meaning.

Name one common application of NLP.

• Chatbots

• Data visualization

• Image processing

• Voice recognition

What is tokenization in NLP?

• Tokenization is the process of combining words into sentences.

• Tokenization is the process of breaking text into smaller units called tokens.

• Tokenization is the method of translating text into numerical values.

• Tokenization refers to the analysis of sentence structure.

Explain the difference between stemming and lemmatization.

• Stemming analyzes the meaning of words, while lemmatization does not.

• Stemming and lemmatization are identical processes that yield the same results.

• Lemmatization is faster than stemming because it uses simpler algorithms.

• Stemming is a crude method that cuts off prefixes or suffixes, while lemmatization uses vocabulary and morphological analysis to return the base form of a word.

What role does machine learning play in NLP?

• Machine learning enhances NLP by enabling algorithms to learn from data for tasks like language understanding and generation.

• Machine learning complicates NLP by removing human input.

• NLP relies solely on rule-based systems without machine learning.

• Machine learning is irrelevant to NLP tasks.

What is sentiment analysis?

• A way to categorize different languages

• Sentiment analysis is the process of determining the emotional tone behind a body of text.

• A technique for summarizing text

• A method for analyzing numerical data

Define named entity recognition (NER).

• A technique for translating languages.

• A process for generating random text.

• Named Entity Recognition (NER) is the process of identifying and classifying named entities in text into predefined categories.

• A method for summarizing text content.

What is the purpose of a language model in NLP?

• The purpose of a language model in NLP is to understand and generate human language.

• To translate text between languages.

• To analyze the sentiment of a text.

• To summarize long documents into bullet points.

How does word embedding work in NLP?

• Word embedding works by representing words as dense vectors in a continuous space, capturing semantic meanings and relationships.

• Word embedding is a method for translating words into different languages.

• Word embedding uses one-hot encoding to represent words as sparse vectors.

• Word embedding relies solely on frequency counts of words in a document.

What challenges does NLP face in understanding context?

• Syntax errors in text

• Limited vocabulary

• Ambiguity, cultural variations, world knowledge, long-range dependencies, sarcasm, idioms, and polysemy.

• Simple sentence structures