CENG0032 Text Retrieval Database Design

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG0032
Name Text Retrieval Database Design
Term 2024-2025 Academic Year
Semester . Semester
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 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

Update Time: 24.05.2024 05:00