Natural Language Processing
| Title | Επεξεργασία Φυσικής Γλώσσας / Natural Language Processing |
| Code | DWS104 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 1st / Undergraduate, 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Grigorios Tsoumakas |
| Common | Yes |
| Status | Active |
| Course ID | 600016258 |
Programme of Study: PMS EPISTĪMĪ DEDOMENŌN KAI PAGKOSMIOU ISTOU (2018 éōs sīmera) PF
Registered students: 8
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 1 | 1 | 7.5 |
| Academic Year | 2023 – 2024 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600239498
|
Course Type 2021
Specialization / Direction
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600239498
- eLearning (Moodle): https://elearning.auth.gr/user/index.php?id=11138
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
General Prerequisites
Programming
Learning Outcomes
Cognitive: Students acquire specialized knowledge on text processing, word embeddings and the analysis of text using statistical language models, the naive Bayes classifier, hidden Markov models and neural networds (feed-forward, recurrent, transformers). In addition, students delve deeper into several text analysis application, such as sentiment analysis, part-of-speech tagging, information extraction, machine translation, question answering and automated summarization.
Skills: Student become acquainted with several libraries for text analysis in Python, including NLTK, spaCy and HuggingFace.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in teams
- Work in an international context
- Generate new research ideas
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Text Processing, Language Modeling with N-Grams, Text Classifiers, Vector Semantics, Neural Nets and Neural Language Models, Sequence Labeling, Deep Learning Architectures for Sequence Processing, Machine Translation and Encoder-Decoder Models, Applications (Information Extraction, Question Answering, Summarization).
Keywords
Natural Language Processing
Educational Material Types
- Slide presentations
- Video lectures
- Interactive excersises
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
slides, interactive notebooks
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Reading Assigment | 62 | ✓ | |||
| Project | 39 | ✓ | |||
| Written assigments | 39 | ✓ | |||
| Exams | 46 | ✓ | |||
| Total | 225 |
Student Assessment
Description
5% written exams, 25% weekly assignments, 25% project
Student Assessment methods
- Written Exam with Short Answer Questions (Summative)
- Oral Exams (Summative)
- Performance / Staging (Summative)
- Written Exam with Problem Solving (Summative)
- Report (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Daniel Jurafsky, James. H. Martin. Speech and Language Processing, 3rd Edition draft, https://web.stanford.edu/~jurafsky/slp3/
Additional bibliography for study
Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python, https://www.nltk.org/book/
Last Update
03-11-2022
