Syllabus Natural Language Processing - [410252(A)] Credit Examination Scheme : 03 In-Sem (Paper) : 30 Marks End-Sem (Paper) : 70 Marks Unit I Introduction to Natural Language Processing Introduction : Natural Language Processing, Why NLP is hard ? Programming languages Vs Natural Languages, Are natural languages regular ? Finite automata for NLP, Stages of NLP, Challenges and Issues(Open Problems) in NLP. Basics of text processing : Tokenization, Stemming, Lemmatization, Part of Speech Tagging. (Chapter - 1) Unit II Language Syntax and Semantics Morphological Analysis : What is Morphology ? Types of Morphemes, Inflectional morphology & Derivational morphology, Morphological parsing with Finite State Transducers (FST). Syntactic Analysis : Syntactic Representations of Natural Language, Parsing Algorithms, Probabilistic context-free grammars, and Statistical parsing. Semantic Analysis : Lexical Semantic, Relations among lexemes & their senses - Homonymy, Polysemy, Synonymy, Hyponymy, WordNet, Word Sense Disambiguation (WSD), Dictionary based approach, Latent Semantic Analysis. (Chapter - 2) Unit III Language Modelling Probabilistic language modeling, Markov models, Generative models of language, Log-Liner Models, Graph-based Models. N-gram models : Simple n-gram models, Estimation parameters and smoothing, Evaluating language models, Word Embeddings / Vector Semantics : Bag-of-words, TFIDF, word2vec, doc2vec, Contextualized representations (BERT). Topic Modelling : Latent Dirichlet Allocation (LDA), Latent Semantic Analysis, Non Negative Matrix Factorization. (Chapter - 3) Unit IV Information Retrieval using NLP Information Retrieval : Introduction, Vector Space Model. Named Entity Recognition : NER System Building Process, Evaluating NER System, Entity Extraction, Relation Extraction, Reference Resolution, Coreference resolution, Cross Lingual Information Retrieval. (Chapter - 4) Unit V NLP Tools and Techniques Prominent NLP Libraries : Natural Language Tool Kit (NLTK), spaCy, TextBlob, Gensim etc. Linguistic Resources : Lexical Knowledge Networks, WordNets, Indian Language WordNet (IndoWordnet), VerbNets, PropBank, Treebanks, Universal Dependency Treebanks. Word Sense Disambiguation : Lesk Algorithm Walker‘s algorithm, WordNets for Word Sense Disambiguation. (Chapter - 5) Unit VI Applications of NLP Machine Translation : Rule based techniques, Statistical Machine Translation (SMT), Cross Lingual Translation Sentiment Analysis, Question Answering, Text Entailment, Discourse Processing, Dialog and Conversational Agents, Natural Language Generation. (Chapter - 6)
