3 Credit Hour Course
Intended For Level 0 Term 0 Students
Prerequisite:
Introduction to Natural Language Processing (NLP): NLP tasks in syntax, semantics, and pragmatics, Applications in information extraction, question answering, and machine translation; N-gram Language Models: The role of language models, Simple N-gram models, Estimating parameters and smoothing, Evaluating language models; Part Of Speech Tagging and Sequence Labeling; Lexical syntax, Hidden Markov Models (Forward and Viterbi algorithms and EM training); LSTM, Recurrent Neural Networks; Syntactic parsing: Grammar formalisms and Treebank, Efficient parsing for context-free grammars (CFGs), Statistical parsing and probabilistic CFGs (PCFGs), Lexicalized PCFGs, Neural shift-reduce dependency parsing; Topic modeling: Latent Dirichlet allocation (LDA); Semantic Analysis: Lexical semantics and word sense disambiguation, Compositional semantics, Semantic Role Labeling and Semantic Parsing; Information Extraction (IE): Named entity recognition and relation extraction, IE using sequence labeling; Sentiment Analysis; Question Answering: Language Generation; Machine Translation (MT): Basic issues in MT, Statistical translation, Word alignment, Phrase-based translation, and synchronous grammars; NLP for Social Media: Fake News Detection, Rumor propagation analysis.
