List any three advantages and disadvantages of NLP.
[3 marks]Define Zipf’s Law and Heap’s Law in the context of NLP with the sutable example.
[4 marks]Explain the various phases of NLP processing from text input to meaningful output.
[7 marks]Explain text pre-processing steps with examples.
[3 marks]Compare bigram and trigram models with a suitable example.
[4 marks]Illustrate how smoothing affects the performance of a language model with an example.
[7 marks]Elaborate on the concept of named entity recognition and its importance in information extraction.
[7 marks]Define Word Sense Disambiguation (WSD). Name two approaches for WSD.
[3 marks]Define the Bag of Words (BoW) model in NLP. Also discuss the limitations of BoW model.
[4 marks]Evaluate WordNet-based WSD vs. supervised WSD for a low-resource language/domain.
[7 marks]Illustrate with an example how Word2Vec captures the context of words.
[3 marks]Differentiate between CBOW and Skip-Gram models with examples.
[4 marks]Discuss the various approaches to Word Sense Disambiguation such as rule-based, statistical, and machine learning approaches with examples.
[7 marks]Explain the concept of sentiment analysis in Natural Language Processing (NLP).
[3 marks]Compare extractive and abstractive summarization with examples.
[4 marks]Describe cross-lingual information retrieval (CLIR) with examples and explain how translation and mapping approaches handle multilingual data.
[7 marks]Page 1 of
[2 marks]Explain the concept of text classification in Natural Language Processing (NLP).
[3 marks]Describe the evaluation metrics used for text summarization.
[4 marks]Analyze bottlenecks in a Question Answering pipeline for multilingual corpora. Explain it with an example.
[7 marks]Define Machine Translation (MT). What is the main objective of machine translation systems?
[3 marks]Compare RBMT and SMT approaches to translation.
[4 marks]Explain Latent Semantic Analysis (LSA) in detail. Include a mathematical formulation using Singular Value Decomposition (SVD).
[7 marks]Define topic modeling. What is the objective of topic models in NLP?
[3 marks]Write short notes on: Latent Variables, and Probabilistic Topic Models
[4 marks]Discuss the importance of EM in SMT parameter learning. Illustrate with an example. Page 2 of
[2 marks]