Computational Linguistics
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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 Computational Linguistics. |
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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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20% Midterm exam + 20% Project (compulsory) + 60% Final exam
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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 |
| 1. |
Introduction to NLP concept and terms |
| 2. |
Normalization, Tokenizing, Lemmatization, Parsing |
| 3. |
Syntax, N-Gram analysis |
| 4. |
Corpus: Features and Analysis |
| 5. |
Part of Speech Tagging, Treebank |
| 6. |
Semantic analysis (probabilistic methods) |
| 7. |
Morphological and Semantic Ambiguity |
| 8. |
Midterm Exam |
| 9. |
Lexical Similarity |
| 10. |
Semantic Similarity |
| 11. |
Dialogue Systems, Question Answering |
| 12. |
Machine Translation |
| 13. |
Keyword Extraction, Document Summarization |
| 14. |
Paraphrasing, Ontology Mapping |
| 15. |
Review for final exam |
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Final Exam |
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