| Course Code | 321-6100 |
|---|---|
| Semester | 7 |
| ECTS | 5.00 |
| Hours (Theory) | 3 |
| Hours (Lab) | 2 |
| Instructor | Stamatatos Efstathios |
Introduction: basic concepts, applications. Morphological analysis, text tokenization and sentence splitting. Language modeling using n-grams. Basic supervised learning methods. Deep learning architectures for classification and sequence labeling. Language modeling using neural networks. Vector semantics, word and document embeddings. Text classification and applications. Part-of-Speech tagging and named-entity recognition. Constituency grammars and parsing, stochastic parsing. Dependency parsing. Logical representations of sentence meaning. Semantic analysis.
Not required.
| Activity | Semester workload |
|---|---|
| Lectures | 52 hours |
| Laboratory hours | 26 hours |
| Personal study | 44 hours |
| Final exams | 3 hours |
Course grading comes from participation in individual laboratory exercises (50%) and written exam (50%). In both cases, lab exercises and written exams, at least 5.0 out of 10 is required. Students are informed about grading policy from the beginning.
Greek (English for Erasmus students)