BÄ°L711 - NATURAL LANGUAGE PROCESSING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| NATURAL LANGUAGE PROCESSING | BÄ°L711 | Any Semester/Year | 3 | 0 | 3 | 8 |
| Prequisites | ||||||
| Course language | English | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Preparing and/or Presenting Reports Problem Solving Project Design/Management | |||||
| Instructor (s) | Department Responsible (bbm-bologna@cs.hacettepe.edu.tr) | |||||
| Course objective | The objective of this course is to teach main paradigms and algorithms in natural language processing. Capabilities and application areas of machine learning algrorithms are taught. | |||||
| Learning outcomes |
| |||||
| Course Content | ? Overview of natural language processing. ? Morphological Processing ? Part-of-Speech Tagging. ? Context Free Grammars, Top-Down and Bottom-Up Parsing, Table Driven Parsing, Unification Grammars. ? Semantic Analysis. ? Apllication of Natural Language Processing. | |||||
| References | 1. Daniel Jurafsky, and James H. Martin, "Speech and Language Processing", Prentice Hall, 2000. 2. James Allen, "Natural Language Understanding", Second edition, The Benjamin/Cumings Publishing Company Inc., 1995. 3. Christopher D. Manning, and Hinrich Schutze, "Foundations of Statistical Natural Language Processing", The MIT Press, 1999. 4. Pierre M. Nugues, ?An Introduction to Language Processing with Perl and Prolog?, Springer, 2006. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Overview of Natural Language Processing |
| Week 2 | Morphological Processing |
| Week 3 | Morphological Processing |
| Week 4 | Statistical Methods |
| Week 5 | Part-of-Speech Tagging |
| Week 6 | Parsing for Context-Free-Languages |
| Week 7 | Parsing Methods for Natutural Languages ? Earley, CYK Parsing Methods |
| Week 8 | Lexicalized and Probabilistic Parsing |
| Week 9 | Semantic Analysis |
| Week 10 | Semantic Analysis |
| Week 11 | Discourse |
| Week 12 | Applications of Natural Language Processing ? Machine Translation |
| Week 13 | Applications of Natural Language Processing ? Information Extraction, Text Summarization |
| Week 14 | Project presentations |
| Week 15 | Final exam study |
| 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 | 50 |
| Seminar | 0 | 0 |
| Midterms | 0 | 0 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 1 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| 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. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest