Week 1

Question 1

What is Natural Language Processing (NLP)?

Answer 1

NLP is the field that focuses on the interaction between computers and human language, aiming to program computers to process and analyse large amounts of natural language data.

Question 2

What are the two primary types of language data in NLP?

Answer 2

Text and Speech/Audio.

Question 3

Name three core applications of NLP.

Answer 3

Machine Translation, Chatbots & Dialogue Systems, and Text Summarization.

Question 4

What is the role of linguistics in NLP?

Answer 4

Linguistics provides insights on language structure and meaning, which help in designing algorithms that can process language accurately.

Question 5

What is Orthography in NLP?

Answer 5

Orthography refers to the conventions of writing in a language, including spelling and punctuation.

Question 6

Define Phonology.

Answer 6

Phonology is the study of sound organization within languages, including pronunciation and intonation.

Question 7

What is Morphology?

Answer 7

Morphology studies the structure of words and how they are formed using roots, prefixes, and suffixes.

Question 8

Explain Syntax.

Answer 8

Syntax is the set of rules that dictates sentence structure, organizing words into phrases and sentences.

Question 9

What is the difference between Semantics and Pragmatics?

Answer 9

Semantics is the study of meaning in language, while Pragmatics focuses on meaning in context, considering how words are used in real situations.

Question 10

Why is Tokenization important in NLP?

Answer 10

Tokenization divides text into individual elements (tokens), which simplifies processing and analysis.

Question 11

What is Named Entity Recognition (NER)

Answer 11

NER identifies real-world entities, like names, locations, or organizations, within text.

Question 12

What is the purpose of Language Models (LMs) in NLP?

Answer 12

Language models predict text sequences, helping in tasks like text generation and machine translation.

Question 13

What are Rule-Based Algorithms in NLP?

Answer 13

These are algorithms that follow predefined human rules to process and analyse language.

Question 14

Describe Supervised Learning in NLP.

Answer 14

Supervised Learning is a machine learning approach where models are trained on labelled data to make predictions (e.g., classifying spam emails).

Question 15

What is Unsupervised Learning in NLP?

Answer 15

Unsupervised Learning identifies patterns in data without labels, such as clustering similar topics in documents.

Question 16

What does Language Modelling in NLP achieve?

Answer 16

Language Modelling predicts the next word in a sequence, aiding in text prediction and generation tasks.

Question 17

Explain Bias in NLP.

Answer 17

Bias in NLP refers to the tendency of models to replicate human biases found in the training data, which can lead to unfair or incorrect predictions.

Question 18

Why is Evaluation important in NLP?

Answer 18

Evaluation measures the performance of NLP models on tasks, ensuring they meet accuracy and relevance standards.

Question 19

What is Machine Translation in NLP?

Answer 19

Machine Translation automatically translates text or speech from one language to another.