Lynkco NLP Introduction Course Manual (EN)

Natural Language Processing Introduction M E Palanivel Professor SITAMS

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Course Objectives & Syllabus Basics of NLP, Morphology, Tokenization, N-gram Models POS Tagging, Parsing, Treebank’s, Ambiguity Handling Word Sense Disambiguation, Semantic Parsing, Sentiment Analysis Machine Translation, Transformers, BERT/GPT, Ethical NLP Speech Recognition, Feature Extraction, Discourse Analysis UNIT I: Introduction to NLP Introduction to NLP: Origins and Challenges, Language and Grammar in NLP, Regular Expressions and Finite-State Automata, Tokenization: Text Segmentation and Sentence Splitting, Morphological Parsing: Stemming and Lemmatization, Spelling Error Detection and Correction, Minimum Edit Distance and Applications, Statistical Language Models:

Unigram, Bigram, and Trigram Models, Processing Indian Languages in NLP.

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UNIT II: Word-Level and Syntactic Analysis Introduction, Part-of-Speech (POS) Tagging: Rule-Based, Stochastic and Transformation-Based Approaches, Hidden Markov Models (HMM) and Maximum Entropy Models for POS Tagging, Context-Free Grammar (CFG) and Constituency Parsing, Treebanks and Normal Forms for Grammar, Top-Down and Bottom-Up Parsing Strategies, CYK Parsing Algorithm, Probabilistic Context-Free Grammars (PCFGs), Feature Structures and Unification UNIT III: Text Classification and Information Retrieval Naпve Bayes Classifier for Text Classification, Training and Optimization for Sentiment Analysis, Information Retrieval: Basic Concepts and Design Features, Information Retrieval Models:

Classical, Non-Classical, and Alternative Models, Cluster Model, Fuzzy Model, and LSTM-Based Information, Retrieval, Word Sense Disambiguation (WSD) Methods: Supervised and Dictionary-Based Approaches.

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UNIT IV: Machine Translation and Semantic Processing Introduction to Machine Translation (MT), Language Divergence and Typology in MT Encoder- Decoder Model for Machine Translation, Translating in Low-Resource Scenarios, MT Evaluation Metrics and Techniques, Bias and Ethical Issues in NLP and Machine Translation, Semantic Analysis and First-Order Logic in NLP, Thematic Roles and Selectional Restrictions in Semantics, Word Senses and Relations Between Senses UNIT V: Speech Processing and Advanced NLP Models Speech Fundamentals: Phonetics and Acoustic Phonetics, Digital Signal Processing in Speech Analysis, Feature Extraction in Speech:

Short-Time Fourier Transform (STFT), Mel-Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP), Hidden Markov Models (HMMs) in Speech Recognition.

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