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
No program outcomes have been defined.
Po-Lo Matrix
The PO-LO matrix has not been populated yet.