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Information
| Code | CENG0032 |
| Name | Text Retrieval Database Design |
| Term | 2022-2023 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
1 |
Course Goal / Objective
The aim of this course is to introduce concepts and methods for knowledge discovery from large amount of text data, and the application of text mining techniques for business intelligence, digital humanities, and social behavior analysis.
Course Content
The course covers basic natural language processing techniques, document representation, text categorization and clustering, document summarization, sentiment analysis, social network and social media analysis, probabilistic topic models and text visualization.
Course Precondition
It is required to have programming skills to apply basic natural language methods.
Resources
Berry, M.W., and Kogan, J., Text Mining: Applications and Theory, Wiley, 2010.
Notes
Recent papers about the course content
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Identifies natural language processing techniques. |
| LO02 | Identifies basic concepts of knowledge discovery from text data. |
| LO03 | Investigates the methods for knowledge discovery from text data. |
| LO04 | Be able to apply text mining techniques to several different areas like business intelligence, digital humanities, and social behavior analysis. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 4 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 3 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | 3 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 3 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 4 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | 1 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 1 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 3 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Natural language processing techniques | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Automatic keyword extraction | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Stopword extraction techniques | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Text representation techniques | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | Text classification techniques | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Matrix factorization for text classification techniques | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 7 | Text clustering techniques | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 8 | Mid-Term Exam | Reading the related papers | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Text summarization techniques | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 10 | Sentiment analysis | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Social network and social media analysis | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 12 | Probabilistic topic modelling | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Text visualization techniques | Reading the related papers | Öğretim Yöntemleri: Anlatım, Tartışma |
| 14 | Multilingual document clustering | Reading the related papers | Öğretim Yöntemleri: Tartışma, Anlatım |
| 15 | Project presentation | Application of the selected algorithms, preparing the project presentation | Öğretim Yöntemleri: Proje Temelli Öğrenme |
| 16 | Writing the project report | Writing the project report | Ölçme Yöntemleri: Proje / Tasarım, Sözlü Sınav |
| 17 | Term Exams | Writing the project report | Ölçme Yöntemleri: Proje / Tasarım, Sözlü Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||