| School/Faculty/Institute |
Faculty of Engineering |
| Course Code |
COMP 451 |
| Course Title in English |
Introduction to Natural Language Processing |
| Course Title in Turkish |
Doğal Dil İşlemeye Giriş |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Introductory |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: - |
Lab: - |
Other: - |
|
| Estimated Student Workload |
152 hours per semester |
| Number of Credits |
6 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
None |
| Expected Prior Knowledge |
Prior knowledge in programming, basic mathematics and probability. |
| Co-requisites |
None |
| Registration Restrictions |
Only Undergraduate Students |
| Overall Educational Objective |
To gain an understanding of computational properties of natural languages, learn different aspects and basic fundamentals of natural language processing, and become familiar with how to design algorithms and applications to process linguistic information. |
| Course Description |
This course covers the fundamentals of natural language processing: morphological analysis, syntactic analysis, parsing, language models, semantics, pragmatic analysis, and evaluation. Moreover, some NLP topics and algorithms are covered such as part-of-speech tagging, word sense disambiguation, dialogue systems, language generation, text classification, summarization, and question answering. |
| Course Description in Turkish |
Bu ders doğal dil işlemede kullanılan temel yöntemleri içermektedir: biçimbilimsel çözümleme, sözdizimsel analiz, bağlılık ayrıştırma, dil modelleri, anlambilim, pragmatik analiz ve performans değerlendirmesi. Ek olarak, kelime türü etiketleme, kelime anlamı belirleme, diyalog sistemleri, dil üretimi, metin sınıflandırma, özet çıkarımı ve soru cevaplama gibi bazı doğal dil işleme konuları ve algoritmaları ele alınmaktadır. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) describe basic principles, algorithms and theoretical issues underlying natural language processing;
2) use probability and statistics to solve linguistic problems;
3) apply computational techniques and tools to process texts written in human languages;
4) analyze and interpret textual data used for natural language processing applications;
5) demonstrate team effort in identifying and solving a complex engineering problem using NLP techniques;
6) acquire and apply new knowledge to prepare a well-organized research report on a selected topic;
7) present research work in front of an audience
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| Program Learning Outcomes/Course Learning Outcomes |
1 |
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6 |
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| 1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
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| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
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| 3) An ability to communicate effectively with a range of audiences |
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| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
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| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
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| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
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| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
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Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
H |
Exam,HW,Project
|
| 2) |
An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
S |
HW,Project
|
| 3) |
An ability to communicate effectively with a range of audiences |
S |
Presentation
|
| 4) |
An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
N |
|
| 5) |
An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives |
H |
Project
|
| 6) |
An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
S |
HW,Project
|
| 7) |
An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. |
S |
Presentation
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| Prepared by and Date |
ŞENİZ DEMİR , December 2020 |
| Course Coordinator |
ŞENİZ DEMİR |
| Semester |
Fall |
| Name of Instructor |
Assoc. Prof. Dr. ŞENİZ DEMİR |
Course Contents
| Week |
Subject |
| 1) |
Introduction to Natural Language Processing |
| 2) |
Morphological Analysis and Disambiguation |
| 3) |
Language Models and N-grams |
| 4) |
Syntactic Analysis and Dependency Parsing |
| 5) |
Semantics, Discourse, and Pragmatics |
| 6) |
Evaluation Methods and Metrics |
| 7) |
Part-of-speech Tagging and Word Sense Disambiguation |
| 8) |
Text Classification and Named Entity Recognition |
| 9) |
Text Summarization |
| 10) |
Dialogue Systems |
| 11) |
Natural Language Generation |
| 12) |
Word Embeddings |
| 13) |
Sequence-to-sequence learning with RNNs |
| 14) |
Research report presentations |
| 15) |
Final Examination/Project/Presentation Period |
| 16) |
Final Examination/Project/Presentation Period |
| Required/Recommended Readings | (Recommended) Speech and Language Processing, D. Jurafsky, J.H. Martin, 2nd Edition, Pearson-Prentice Hall, 2009.
(Supplementary) Foundations of Statistical Natural Language Processing, C.D. Manning, H. Schutze, MIT Press, 2002.
(Supplementary) Natural Language Processing with Python, S.Bird, E.Klein, E.Loper, O’Reilly Media, 2009. |
| Teaching Methods | Flipped Classroom |
| Homework and Projects | In-Class flipped Practices, Projects |
| Laboratory Work | None |
| Computer Use | Required |
| Other Activities | None |
| Assessment Methods |
| Assessment Tools |
Count |
Weight |
| Homework Assignments |
1 |
% 15 |
| Presentation |
1 |
% 20 |
| Project |
1 |
% 35 |
| Midterm(s) |
1 |
% 30 |
| TOTAL |
% 100 |
|
| Course Administration |
demirse@mef.edu.tr
535
Instructor’s office: Room 536 (5th floor)
Office hours: TBA.
E-mail address: demirse@mef.edu.tr
Rules for attendance: No attendance required
A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Statement on plagiarism: YÖK Regulations |
Information Package / Course Catalogue