NATURAL LANGUAGE PROCESSING
- Course
- AIEN422 - NATURAL LANGUAGE PROCESSING
- Department
- Artificial Intelligence Engineering - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 5
- T+P+L
- 3 + 0 + 1
- Course Coordinator(s)
- -
- Prerequisite
- -
Course Description
Natural Language Processing course covers topics to convert text data to processable information. Initial part of this course contains preliminary data processing to create feature matrix that will also cover stemming, lemmatization, part of speech tagging, bag of words, n-grams, stop words, normalization, idf, tf/idf. Distance metrics together with evaluation metrics (F-MEASUE, BLUE, ROUGE) will also revised in this course. Semantic feature extraction that covers named entity recognition, word sense disambiguation and dimensionality reduction with factorization methods will also be discussed. Application of variety number of machine learning techniques for text data is also covered in this course. Last section focuses on real world application like text summarization, author identification, text classification and categorization.
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 Natural Language Processing (NLP) and Information Retrieval (IR) |
| Week 2 | NLP Application Areas; text recognition, sentiment analysis, document clustering, question-answering |
| Week 3 | Pre-Processing for NLP: Tokenization, stop word removal, normalization, lammitization, regular expressions, morphological analysis |
| Week 4 | Vector space model, cosine similarity |
| Week 5 | Retrieval of documents: tf x idf and evaluation Methods |
| Week 6 | Neural Networks and Deep Learning for IR and NLP - Recent Trends |
| Week 7 | Artificial Neural Networks (ANN) - Perceptron |
| Week 8 | Multi-Layer ANNs |
| Week 9 | Midterm |
| Week 10 | Recurrent Neural Networks, Long-Short Term Memory Networks (LSTM) |
| Week 11 | Convolutional Neural Networks (CNNs) |
| Week 12 | Convolutional Neural Networks (CNNs) for NLP tasks |
| Week 13 | Attention Networks |
| Week 14 | Semantic Web for data representation |
| Week 15 | SPARQL for querying semantic data |
Reference Books & Course Materials
No reference books have been listed.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in complex engineering problems.
- Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
- Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose.
- Ability to devise, select, and use modern techniques and tools needed for analysing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively.
- Ability to design and conduct experiments, gather data, analyse and interpret results for investigating complex engineering problems or discipline specific research questions.
- Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
- Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
- Recognition of the need for lifelong learning ; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
- Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice.
- Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development.
- Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.
Po-Lo Matrix
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