Natural Language Processing(INF 513)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| INF 513 | Natural Language Processing | 1 | 3 | 0 | 0 | 3 | 6 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Elective |
| Course Level | Masters Degree |
| Course Instructor(s) | İsmail Burak PARLAK bparlak@gsu.edu.tr (Email) |
| Assistant | |
| Objective | Teaching theoretical fundamentals, tools, concepts and methodologies for manipulating textual information with computers. |
| Content | - Introduction to Natural Language Processing (NLP) - Finite States Machine and NLP - Applications: Nooj, Unitex,… - N-grams - POS Tagging - Application: Porter Stemmer, Brill Tagger,… - HMM - Syntactic Parsing - Semantic - Application: Building a chunk parser - Computational lexical semantics - Computational discourse - Information extraction, temporal event extracting - Application: Named Entities recognition |
| Course Learning Outcomes |
- Acquiring main concepts, tools and techniques of Natural Language Processing - Being able to build an application related to these domain |
| Teaching and Learning Methods | |
| References |
- Foundation of Statistical Natural Language Processing, C.D. Manning & H. Schütze, The MIT Press, 6th ed, 2003 - Speech and Language Processing, D. Jurafsky & J.H. Martin, Pearson, 2009 - Natural Language Processing with Python, Steven Bird, Ewan Klein, Edward Loper, O'Reilly Media, June 2009 |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Regular Expressions, Text Normalization, Edit Distance |
| 2 | Finite State Transducers, Spelling Correction |
| 3 | Neural Nets, Deep Learning Models-I |
| 4 | Deep Learning Models-II |
| 5 | Hidden Markov Models, ngrams, stochastic language models |
| 6 | Part of Speech (POS) Tagging, Formal Grammars |
| 7 | Syntactic and morphological analyzers |
| 8 | Lexicons; design and implementation |
| 9 | Computatinal semantics, information extraction |
| 10 | Text classification, text summarization |
| 11 | Machine translation, question-answering systems |
| 12 | Speech Analysis-I |
| 13 | Speech Analysis-II: synthesis, recognition |
| 14 | Projects |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 1 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 2 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 2 | 20 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 0 | 0 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 1 | 40 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Toplam | 3 | 60 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
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| 6 | X | |||||
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| 11 | X | |||||
| 12 | X | |||||
| 13 | X | |||||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Total Workload | 0 | ||
| Total Workload / 25 | 0.00 | ||
| Credits ECTS | 0 | ||