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?

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.

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 concept of partial parsing in the context of constituency parsing. How does partial parsing differ from full parsing, and what applications benefit from partial parsing techniques?

Explain Combinatory Categorical Grammar. List and explain grammar rules for English.

Describe dependency parsing and the notion of dependency relations between words in a sentence. Compare and contrast dependency parsing with constituency parsing, highlighting their respective strengths and weaknesses.

Write short note on : i) Context Free Grammar ii) Partial Parsing iii) Describe Chomsky’s Normal Form (CNF)

Examine the challenges associated with thematic roles in semantic role labeling. Discuss how issues such as selection restrictions and decomposing predicates influence the accuracy of semantic role labeling systems.

Define connotation frames and explain their significance in understanding the nuanced meaning of words and phrases. Discuss how connotation frames can be utilized in various natural language processing tasks, such as affect recognition and sentiment analysis.

Define word senses and explain the importance of disambiguating word senses in natural language processing tasks. Discuss the challenges associated with word sense disambiguation (WSD) and how it relates to the ambiguity of language.

Explore the use of lexicons for sentiment recognition in natural language processing. Discuss how sentiment lexicons are utilized to classify the sentiment of words and phrases in text data, highlighting their effectiveness and limitations.

Describe the components and functionalities of a Question Answering (QA) system. How do QA systems process natural language questions, retrieve relevant information from textual sources, and generate concise answers? Discuss the challenges associated with QA systems.

Explain the concept of Direct Machine Translation and its underlying principles. Discuss the advantages and disadvantages of this approach in comparison to other MT methods.

Explore Knowledge-Based Machine Translation systems and their reliance on domain-specific knowledge bases. How does knowledge-based MT differ from rule-based and statistical approaches? Discuss the role of semantic knowledge in improving translation.

Describe the architecture and operation of Rule-Based Machine Translation systems. How do RBMT systems utilize linguistic rules and dictionaries to generate translations? Discuss the challenges associated with rule-based approaches.

Explain the Vector Space Model (VSM) in Information Retrieval. How does VSM represent documents and queries as vectors in a high-dimensional space? Discuss the cosine similarity measure and its role in ranking documents for a given query.

Discuss the ethical considerations and challenges associated with deploying NLP- based systems, such as bias in training data, privacy concerns, and the potential for misuse. How can developers mitigate these issues and ensure responsible use of NLP technologies in real-world applications?

Discuss the use of sequence labeling in Information Extraction tasks. What are the key sequence labeling techniques used for tasks such as Named Entity Recognition (NER) and Part-Of-Speech (POS) tagging? Provide examples to illustrate their applications.

Explain how NLTK (Natural Language Toolkit) facilitates text analysis and processing tasks in Python. Discuss the key features and functionalities provided by NLTK for tasks such as tokenization, stemming, lemmatization, and syntactic parsing.

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

Explain Combinatory Categorial Grammar.

List and Explain grammar rules for English.

Explain partial parsing with example.

Discuss Advanced Methods in Transition-Based Parsing.

Explain Word Sense Induction.

Explain Features-based Algorithm for Semantic Role Labeling.

Explain Connotation Frames.

Explain defining emotions with Plutchik wheel of emotion.

Explain need of 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.