AL504 (B) - Natural Language Processing

Define Natural Language Processing (NLP) and explain its complete historical evolution from rule-based systems to modern deep learning based NLP models. Discuss in detail the major challenges in natural language understanding, ambiguity resolution, context handling and multilingual processing with suitable real-world examples.

Explain the levels of language analysis in NLP. Describe lexical, syntactic, semantic, discourse and pragmatic analysis with examples.

Explain tokenization, stemming and lemmatization in detail. Compare stemming and lemmatization and discuss their importance in NLP applications.

Describe stop word removal and normalization techniques. Explain how text pre-processing improves NLP system performance.

Explain Part-of-Speech (POS) tagging in detail. Describe different approaches used for POS tagging such as rule-based, stochastic and neural approaches.

Discuss ambiguity in natural language processing. Explain lexical ambiguity and syntactic ambiguity with suitable examples.

Explain syntax analysis in NLP. Describe top-down and bottom-up parsing techniques with suitable examples.

Explain Context Free Grammar (CFG) and parse trees. Discuss their role in syntactic analysis.

Explain semantic analysis in Natural Language Processing. Discuss meaning representation techniques, semantic roles, thematic relations and logical forms. Explain how semantic analysis helps in tasks such as question answering, information extraction and machine translation.

Discuss Word Sense Disambiguation (WSD). Explain different approaches used for WSD with examples.

Explain language models in NLP. Describe N-gram language models and discuss their advantages and limitations.

Explain neural language models. Compare traditional statistical language models with neural language models.

Explain Machine Translation in Natural Language Processing. Describe rule-based statistical and neural machine translation approached in detail. Compare these approaches in terms of accuracy, scalability and real-world applications.

Discuss major applications of NLP in real-world systems such as information retrieval, sentiment analysis and chatbots.

Explain Speech Recognition systems and their working principles.

Explain Text Summarization techniques and their applications.