Introduction to Natural Language Processing. The Course Presents Methods For Representing Human Language at Different Levels, Moving From Simple Bag-of Words Representations, Through Sequential Models, To Syntactic and Semantic Structures. at Each Level, The Course Develops Several Machine Learning Models and Algorithms For Solving Various Natural Language Processing Tasks, Such As Text Classification, Sequence Labeling, Syntactic Parsing, and Semantic Parsing. The Course Discusses Models Such As Naive Bayes, Logistic Regression, Hidden Markov Models, Various Deep Neural Networks And Appropriate Learning Algorithms For Estimating Their Parameters. The Course Emphasizes Implementation of Classical and Modern Natural Language Processing Models. Learning Ouctomes# By The End of The Course The Student Will Be Able To# 1. Model The Standards Levels Of Linguistic Structures Using Formal Grammars Or Statistical And Computational Models. 2. Identify and Carry Out Proper Experimental For Training and Evaluating Natural Language Processing Systems. 3. Manipulate Probabilities and Estimate Parameters of Structured Models Using Supervised Training Methods. 4. Implement Simple Models Of Language and Employ and Adapt Theim in Service of Solving Natural Language Processing Problems.

Faculty: Computer Science
|Undergraduate Studies |Graduate Studies

Pre-required courses

234247 - Algorithms 1

Course with no extra credit

97215 - Methods in Natural Language Processing 97216 - Natural Language Processing

Semestrial Information