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Natural Language Processing (ΠΛ6): Διαφορά μεταξύ των αναθεωρήσεων

Natural Language Processing (ΠΛ6): Διαφορά μεταξύ των αναθεωρήσεων

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Αναθεώρηση της 19:56, 10 Νοεμβρίου 2022

Graduate Courses Outlines - Department of Mathematics

General

School School of Science
Academic Unit Department of Mathematics
Level of Studies Graduate
Course Code ΠΛ6
Semester 1
Course Title Natural Language Processing
Independent Teaching Activities Lectures (Weekly Teaching Hours: 3, Credits: 7.5)
Course Type Specialization
Prerequisite Courses

Undergraduate courses in Automata Theory and Formal Languages, Introduction to Natural language Processing.

Language of Instruction and Examinations

Greek

Is the Course Offered to Erasmus Students Yes (in English)
Course Website (URL) See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes

The goal of this course is the deeper understanding of Natural Language Processing which concern to:

  • the NL linguistics data formalization
  • the codification of the NL syntax, morphology and semantics structure rules
  • the parsing and generation algorithms of NL sentences

as well as the introduction of students to critical thinking and research process. During the course a detailed examination of the above topics is done. After completing the course the student can handle theoretical documentation of problems and solving exercises, which are related to:

  • definition and design of syntactic structure or phrase structure grammars as well as algorithms and syntactic analysis technics.
  • formalization of morphological rules, design data bases and expert systems as well as algorithms and morphological analysis technics.
  • formalization of semantic rules, design data bases and expert systems as well as algorithms and semantic analysis technics.
General Competences
  • Independent work
  • Bibliographic search
  • Effective selection and Design of the required machine and language.

Syllabus

  • Properties of the Computation Theory Mathematical Models
  • Problems classification to solvable and unsolvable
  • Solvable Problems Classification

Teaching and Learning Methods - Evaluation

Delivery

Face to face

Use of Information and Communications Technology Yes
Teaching Methods
Activity Semester Workload
Lectures 39
Self study 78
Exercises 70.5
Course total 187.5
Student Performance Evaluation
  • Final essays (40%) .
  • Exercises -questions requiring critical thinking (30%).
  • Presentations of related issues (30%).

Attached Bibliography

  • Mitkov Ruslan, The Oxford Handbook of Computational Linguistics. ISBN 0-19-823882
  • Jurafsky Daniel & Martin H. James Speech and Language Processing - An Introduction to Ntural Language Proocessing, Computational Linguistics and Speech Recognition. ISBN 0-13-095069-6
  • ALLEN James Natural Language Understanding. ISBN 0-8053-0334-0
  • Natural Language Generation ed. by Gerard Kempen. ISBN 90-247-3558-0.
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