INFORMATION RETRIEVAL FOR NATURAL LANGUAGE PROCESSING
- Course
- CMPE640 - INFORMATION RETRIEVAL FOR NATURAL LANGUAGE PROCESSING
- Department
- Computer Engineering - English - PhD
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
- -
Course Description
-
INFORMATION RETRIEVAL FOR NATURAL LANGUAGE PROCESSING
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction - Information Retrieval (IR) and Natural Language Processing (NLP) |
| Week 2 | IR data models; vector space model, probabilistic models, word2vec, GloVe, semantic and others |
| Week 3 | IR Techniques |
| Week 4 | IR Techniques Cont. and IR Evaluation Methods |
| Week 5 | Semantic Web for data representation |
| Week 6 | NLP Application Areas; text recognition, sentiment analysis, document clustering, question-answering |
| Week 7 | Stemming, morphological, syntactic, semantic analysis, lemmitization, regular expressions |
| Week 8 | Midterm week |
| Week 9 | Neural Networks and Deep Learning for IR and NLP - Recent Trends |
| Week 10 | Neural Networks, Convolutional Neural Networks |
| Week 11 | Transformers, state of the art language model for NLP - BERT (Bidirectional Encoder Representations from Transformers) |
| Week 12 | Attention Networks |
| Week 13 | Graph Convolutional Networks |
| Week 14 | Project presentations |
| Week 15 | Final Exam Week |
Reference Books & Course Materials
- 01 Information Retrieval Models, Springer.
- 02 Natural Language Processing with Phyton - Free eBook (https://www.nltk.org/book/)
- 03 Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (1st Edition) (e-book)
- 04 Deep Learning for Natural Language Processing, https://www.manning.com/books/deep-learning-for-natural-language-processing
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate mastery of advanced research methodologies (quantitative, qualitative, and mixed methods) by critically analyzing literature, identifying research gaps, and designing original studies that contribute to MIS theory and practice.
- Conduct and defend an original doctoral dissertation that reflects independent scholarly inquiry, academic rigor, and a significant contribution to the advancement of knowledge in MIS.
- Exhibit readiness for thesis monitoring and defense by articulating the philosophical foundations of research paradigms, positioning one's research within these frameworks, and responding to scholarly critique.
- Apply ethical principles, academic integrity, and responsible conduct of research in all phases of the research process, including data collection, analysis, reporting, and publication.
- Identify and address the social, legal, and ethical implications of information systems research and its applications within organizational and societal contexts.
- Employ advanced data science techniques, including statistical modeling, machine learning, and AI-based analytics, to examine complex datasets and extract meaningful insights in MIS research.
- Recognize and evaluate emerging technologies such as artificial intelligence, big data, blockchain, and the Internet of Things, assessing their transformative impact on organizational processes and digital ecosystems.
- Collaborate and lead in interdisciplinary research environments, establishing productive scientific partnerships and managing research projects that integrate diverse academic perspectives.
- Publish high-quality research in peer-reviewed journals, present findings at international conferences, and actively engage in academic service such as journal reviewing, conference organizing, and committee participation.
- Identify challenges and propose innovative, research-based solutions at the intersection of information systems, technology, and organizational strategy.
- Deliver advanced-level MIS courses, supervise graduate research, and nurture academic development through effective mentorship and scholarly teaching.
- Develop advanced information systems and decision support systems that align IT capabilities with organizational strategies using systems thinking and innovative design methodologies.
Po-Lo Matrix
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