BÄ°L670 - STATISTICAL NATURAL LANGUAGE PROCESSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| STATISTICAL NATURAL LANGUAGE PROCESSING | BÄ°L670 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | none | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Discussion Question and Answer | |||||
| Instructor (s) | Asst. Prof. Dr . Burcu Can Buğlalılar | |||||
| Course objective | To maket he student have an understanding on Statistical Learning of Natural Language. | |||||
| Learning outcomes |
| |||||
| Course Content | ? Probabilistic Language Modelling ? Probabilistic Context Free Grammars (PCFGs) ? Parsing with PCFGs ? Collocations and Clustering ? Part-of-Speech Tagging and Hidden Markov Models ? Bayesian Language Modelling ? NonParametric Bayesian Language Modelling ? Word Class Induction By Distributional | |||||
| References | ? Christopher D. Manning, and Hinrich Schutze, "Foundations of Statistical Natural Language Processing", The MIT Press, 1999. ? Daniel Jurafsky, and James H. Martin, "Speech and Language Processing", Prentice Hall, 2000. ? James Allen, "Natural Language Understanding", Second edition, The Benjamin/Cumings Publishing Company Inc., 1995. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to Natural Language Processing |
| Week 2 | Grammars (Context free grammars, parsing) |
| Week 3 | Review of Probability Concepts |
| Week 4 | Classification and Introduction to Information Theory |
| Week 5 | Probabilistic Language Modelling (N-gram, smoothing, noisy channel) |
| Week 6 | Collocations and Clustering |
| Week 7 | Part-of-Speech Tagging and Hidden Markov Models |
| Week 8 | Word Class Induction by Distributional Models |
| Week 9 | Word Sense Disambiguation and Expectation Maximization |
| Week 10 | Probabilistic Context Free Grammars (PCFGs) |
| Week 11 | Parsing with PCFGs |
| Week 12 | Bayesian Language Modelling |
| Week 13 | Non-parametric Bayesian Language Modelling |
| Week 14 | Machine Translation with IBM Model |
| Week 15 | Final exam preparation |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Project | 1 | 60 |
| Seminar | 0 | 0 |
| Midterms | 0 | 0 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 0 | 60 |
| Percentage of final exam contributing grade succes | 0 | 40 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 1 | 70 | 70 |
| Homework assignment | 0 | 0 | 0 |
| Midterms (Study duration) | 0 | 0 | 0 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total Workload | 30 | 108 | 212 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Graduates should have a mastery of computer science as described by the core of the Body of Knowledge. | X | ||||
| 2. Graduates need understanding of a number of recurring themes, such as abstraction, complexity, and evolutionary change, and a set of general principles, such as sharing a common resource, security, and concurrency. | X | ||||
| 3. Graduates of a computer science program need to understand how theory and practice influence each other. | X | ||||
| 4. Graduates need to think at multiple levels of detail and abstraction. | X | ||||
| 5. Students will be able to think critically, creatively and identify problems in their research. | X | ||||
| 6. Graduates should have been involved in at least one substantial project. | X | ||||
| 7. Graduates should realize that the computing field advances at a rapid pace. | X | ||||
| 8. Graduates should conduct research in an ethical and responsible manner. | X | ||||
| 9. Graduates should have good command of technical terms in both Turkish and English. | X | ||||
| 10. Graduates should understand the full range of opportunities available in computing. | X | ||||
| 11. Graduates should understand that computing interacts with many different domains. | X | ||||
| 12. Graduates should develop the knowledge acquired at master level and apply scientific methods in order to solve scientific problems. | |||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest