Introducing Natural Language and its applications; Implementation of Natural Language Processing (NLP) applications.
Prerequisite(s)
-
Corequisite(s)
-
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Instructor Erkan ÇELİK
Course Assistant(s)
-
Schedule
Saturday Theory:09.00-11.00 Lab:11.00-13.00
Office Hour(s)
-
Teaching Methods and Techniques
Lecture, Discussion, Research
Principle Sources
Speech and Language Processing, Jurafsky and Martin, Prentice Hall
x
Other Sources
Foundations of Statistical Natural Language Processing, C. D. Manning, H. Schütze, MIT
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to Natural Language Processing
Oral and written presentation.
2. Week
Language models, N-grams.
Oral and written presentation.
3. Week
Grammar and languages.
Oral and written presentation.
4. Week
Syntactic Analysis
Oral and written presentation.
5. Week
Regular expressions
Oral and written presentation.
6. Week
Morphological analysis.
Oral and written presentation.
7. Week
Midterm.
Midterm.
8. Week
Corpora for NLP, Machine learning
Oral and written presentation.
9. Week
Text classification.
Oral and written presentation.
10. Week
Information retrieval
Oral and written presentation.
11. Week
Text indexing and retrieval
Oral and written presentation.
12. Week
Question answering
Oral and written presentation.
13. Week
Hidden markov Models
Oral and written presentation.
14. Week
Project presentations
Oral Presentation.
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
30
Homework / Term Projects / Presentations
1
30
Final Exam
1
40
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Describe the typical problems and processing layers in NLP
Compose key NLP elements to develop higher level processing chains
LO-5
Analyze NLP problems to decompose them in adequate independant components
LO-6
Understand natural language processing and to learn how to apply basic algorithms in this field.
LO-7
Get acquainted with the algorithmic description of the main language levels: morphology, syntax, semantics, and pragmatics, as well as the resources of natural language data - corpora.
LO-8
Conceive basics of knowledge representation, inference, and relations to the artificial intelligence.
