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Subject Syllabus

AL504 (B) - Natural Language Processing

AIML · Semester 5

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

Origins and challenges of NLP Language modeling: grammar-based language models and statistical language models Regular expressions Finite-state automata English morphology Transducers for lexicon and rules Tokenization Detecting and correcting spelling errors Minimum edit distance

Unsmoothed n-grams Evaluating n-grams Smoothing, interpolation, and backoff Word classes Part-of-speech tagging Rule-based, stochastic, and transformation-based tagging Issues in part-of-speech tagging Hidden Markov models and maximum entropy models Viterbi algorithm and EM training

Context-free grammars Grammar rules for English Treebanks Normal forms for grammar Dependency grammar Syntactic parsing and ambiguity Dynamic programming parsing Shallow parsing Probabilistic context-free grammars Probabilistic CYK Probabilistic lexicalized context-free grammars Feature structures and unification of feature structures

Requirements for representation First-order logic Description logics Syntax-driven semantic analysis Semantic attachments Word senses and relations between senses Thematic roles and selectional restrictions Word sense disambiguation WSD using supervised, dictionary, thesaurus, and bootstrapping methods Word similarity using thesaurus and distributional methods Compositional semantics

Intelligent work processors Machine translation User interfaces and man-machine interfaces Natural language querying Tutoring and authoring systems Speech recognition Commercial use of NLP