Explain Context Free Grammar and Grammar rules For English in detail.

Write short note based on constituency parsing. i) Ambiguity ii) Partial Parsing iii) CCG Parsing

Elaborate dependency relations and dependency formalism of dependency parsing.

Write short note based on constituency parsing. i) Ambiguity ii) Span based neural constituency parsing iii) CKY Parsing

Explain Word senses and relation between various senses.

Explain lexicon for sentiment-Emotions, sentiment and affect lexicons, Creating Affect Lexicons by Human Labeling with suitable example.

Write down about WordNet and wordsense disambituition in detail.

Explain lexicon for sentiment-Semi-supervised Induction of Affect Lexicons, Supervised Learning of Word Sentiment, Using Lexicons for Sentiment. Recognition with suitable example.

Explain need ot Machine Translation (MT) with suitable example. Which are the problems of Machine Translation?

Write short note on: i) Knowledge based MT System ii) Encoder-decoder architecture

Explain Machine Translation (MT) approaches with suitable example. Describe Direct Machine Translation in detail.

Write short note on: i) Statistical Machine Translation (SMT) ii) Neural Machine Translation

Elaborate Information retrieval-Vector space Model in detail.

Write short note on: i) Categorization ii) Summarization iii) Sentiment Analysis

Discuss Information Extraction using Sequence Labelling in detail.

Write short note on: i) Named Entity Recognition. ii) Analyzing text with NLTK iii) Chatbot using Dialogflow

Consider the following CNF rules. Create a Parse tree for the sentence “The flight includes a meal” using CKY parsing algorithm. S  NP VP, NP  Det N, VP  V NP, V  includes, Det  the, Det  a, N  meal, N  flight

Explain why CFG is used to represent natural language in parsing. Differentiate between top-down and bottom-up parsing.

Consider following grammar rules. S  NP VP, S  VP, NP  DET N, NP  N, VP  V, VP  V NP, Det  this | that | a | the, Noun  book | flight | John | ball | meal, Verb  book | include | read. Generate the Top-Down and Bottom-up Parse Trees for the sentence. “Book that flight”. Is the Top-Down parsing approach better than Bottom up approach? Justify your answer.

What is Constituency Parsing? Explain CCG parsing with an example.

What do you mean by Semantic and Thematic Roles? List out any 4 thematic roles with definitions and examples.

Write short note on : i) WordNet ii) FrameNet

What is the significance of Word Sense Disambiguation in NLP? Explain any one Word Sense Disambiguation method.

Explain the Scherer typology of affective states. What are the two families of theories of emotion?

Why is Machine Translation needed? Explain various problems of machine translation.

Explain in detail Rule based Machine Translation, Knowledge based Machine Translation and Statistical Machine Translation.

Draw a neat diagram of Encoder-decoder architecture. Explain the working of Neural Machine Translation.

Explain the stages of a Direct Machine Translation System with example.

Write short notes on : i) Named Entity Recognition ii) Question Answer System iii) Chatbot using Dialogflow

Draw the architecture of an ad hoc Information Retrieval system. Explain the working of vector space model of information retrieval.

Describe the following approaches used in information retrieval. i) Term weighting and document scoring ii) Stop word Elimination iii) Inverted Index

Explain the stages and working of Question Answering System.

Explain Generic Natural Language Processing System in detail.

List and explain the challenges of Natural Language Processing.

Describe knowledge-based approaches used in NLP.

List and explain different Levels of Natural Language Processing.

Explain the applications of Natural Language Processing.

Describe rule-based approaches used in NLP.

What is Morphology? Which are the types of Morphology?

Explain Morphological Parsing with Finite-State Transducers.

Discuss the term Word and Sentence Tokenization.

Describe N-gram for language model using suitable example.

Explain Orthographic Rules and Finite-State Transducers.

Explain Derivational & inflectional morphology in detail.

Explain the different levels of language analysis.

List any three challenges in NLP. Provide solution to these challenges.

Compare Rule based, Data Based and knowledge Based approaches of NLP.

With a neat diagram describe how a typical NLP system is organised.

Explain the working of Rule based approach for NLP.

Explain why ambiguity is one of the core challenges of NLP. Give examples.

Define Morphology. Explain stem and affix classes of Morphemes with examples.

Explain Minimum Edit Distance Algorithm.

Explain Morphological Parsing with Finite-State Transducers.

Why do we need a 3-tape FST for morphological parsing. Illustrate with an example.

Explain the spelling Correction approaches in NLP.

Explain the use of Finite State Automata for Morphological Analysis.

Define the Natural language processing. Elaborate the applications and challenges of NLP.

What role does knowledge play in language processing, and how is ambiguity addressed in natural language?

Describe the text pre-processing method in NLP.

In what ways do rule-based, data-based, and knowledge-based approaches impact NLP development?

How is the effectiveness of the English to Marathi translation analyzed? Explain with suitable example.

What are the key concepts in Finite-State Morphological Parsing?

How does the porter stemmer enhance text processing, and what benefits does it offer in linguistic analysis?

In spelling error detection and correction, how does the concept of minimum edit distance help improve accuracy, and what methods are used to implement it effectively?