Terminology extraction and named entity recognition
| Title | Αυτόματη εξαγωγή ορολογίας με έμφαση στην ορολογία και στις ονοματικές οντότητες / Terminology extraction and named entity recognition |
| Title in French | Extraction automatique de terminologie et reconnaissance des entités nommées |
| Code | Ε329 |
| Faculty | Philosophy |
| School | French Language and Literature |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter/Spring |
| Common | No |
| Status | Active |
| Course ID | 600018722 |
Programme of Study: PPS Tmīmatos Gallikīs Glṓssas kai Filologías (2020-)
Registered students: 1
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Mandatory Elective Courses | Winter/Spring | - | 6 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600284354
|
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600284354
- eLearning (Moodle):
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction)
- French (Instruction, Examination)
Learning Outcomes
After having successfully completed the course, students have become familiar with the natural language processing systems Unitex and Termostat
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Work autonomously
- Work in teams
- Generate new research ideas
Course Content (Syllabus)
Creation of linguistic tools for information extraction. In particular, we are on the borderline between lexical analysis and syntactic analysis. The course examines mainly, technical words (simple and compound), certain technical or specialized expressions (for instance dates or acronyms), disambiguation by applying grammars, which need a context recognition. Grammars are implemented in the form of finite automata or finite transducers :
1. Introduction to information extraction
2. Extracting different types of information
3. Ambiguity issues
4. Tools
5. Tools
6. Methods
7. Methods
8. Creating terminology extraction grammars
9. Creating terminology extraction grammars
10. Creating named entity extraction grammars (example: dates)
11. Creating named entity extraction grammars (example: person names)
12. Creating dictionary graphs
13. Reflection on ambiguities
Keywords
information extraction, specialized expressions
Educational Material Types
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
Description
80% of the course is computer-based. The final evaluation project is also computer-based.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | 1.6 | |||
| Laboratory Work | 49 | 2.0 | |||
| Project | 30 | 1.2 | |||
| Written assigments | 30 | 1.2 | |||
| Exams | 2 | 0.1 | |||
| Total | 150 | 6 |
Student Assessment
Description
Evaluation will be based on a final project.
Student Assessment methods
- Report (Formative)
- Labortatory Assignment (Formative)
Bibliography
Course Bibliography (Eudoxus)
Kyriacopoulou T. (2005), L'analyse automatique des textes écrits: le cas du grec, Thessaloniki: University Studio Press.
Last Update
07-02-2020
