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)
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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
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Po-Lo Matrix
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