Natural Language Processing
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Course Objectives
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| In this course, the main goal is to define the methods and approaches used in Natural Language Processing. |
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Course materials
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- Daniel Jurafsky and James H. Martin, Speech and language processing an introduction to natural language processing, computational linguistics, and speech, 2000.
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Assessment
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40% Midterm (exam,tasks,etc.) + 60% Final (exam,tasks,etc.)
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Prerequisites
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there is no formal prerequisite, to get theory of computation (automata theory) course before is recommended.
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| Week |
Subjects |
Note |
| 1. |
Introduction to NLP: Concepts and terms |
Lesson 1 |
| 2. |
Text Normalization, Lemmatization, Parsing |
Lesson 2 |
| 3. |
N-Grams and Language Models |
Lesson 3 |
| 4. |
Corpus (Features and Analysis) |
Lesson 4 |
| 5. |
Part of Speech Tagging |
Lesson 5 |
| 6. |
Introduction to Semantic Analysis |
Lesson 6 |
| 7. |
Ambiguity |
Lesson 7 |
| 8. |
Midterm Exam |
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| 9. |
Lexical Similarity |
Lesson 8 |
| 10. |
Semantic Similarity |
Lesson 9 |
| 11. |
Dialogue Systems, Question Answering |
Lesson 10 |
| 12. |
Machine Translation |
Lesson 11 |
| 13. |
Keyword Extraction, Document Summarization |
Lesson 12 |
| 14. |
Paraphrasing, Ontology Mapping |
Lesson 13 |
| 15. |
Review for final exam |
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Final Exam |
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