Natural Language Processing (ΠΛ6): Διαφορά μεταξύ των αναθεωρήσεων

Τελευταία αναθεώρηση της 05:17, 16 Ιουνίου 2023

Ελληνική Έκδοση
Graduate Courses Outlines
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Department of Mathematics
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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.

@@ Γραμμή 1: / Γραμμή 1: @@
-[[Graduate Courses Outlines]] - [https://math.uoi.gr  Department of Mathematics]
+* [[Επεξεργασία Φυσικής Γλώσσας (ΠΛ6)|Ελληνική Έκδοση]]
+{{Course-Graduate-Top-EN}}
+{{Menu-OnAllPages-EN}}
 === General ===
@@ Γραμμή 15: / Γραμμή 17: @@
 |-
 ! Course Code
-| ΧΧΧ
+| ΠΛ6
 |-
 ! Semester
-| 000
+| 1
 |-
 ! Course Title
-| ΧΧΧ
+| Natural Language Processing
 |-
 ! Independent Teaching Activities
@@ Γραμμή 27: / Γραμμή 29: @@
 |-
 ! Course Type
-| General Background
+| Specialization
 |-
 ! Prerequisite Courses
-| -
+|
+Undergraduate courses in Automata Theory and Formal Languages, Introduction to Natural language Processing.
 |-
 ! Language of Instruction and Examinations
 |
-ΧΧΧ
+Greek
 |-
 ! Is the Course Offered to Erasmus Students
-| Yes
+| Yes (in English)
 |-
 ! Course Website (URL)
@@ Γραμμή 49: / Γραμμή 52: @@
 ! Learning outcomes
 |
-ΧΧΧ
+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
 |
-ΧΧΧ
+* Independent work
+* Bibliographic search
+* Effective selection and Design of the required machine and language.
 |}
 === Syllabus ===
-ΧΧΧ
+* Properties of the Computation Theory Mathematical Models
+* Problems classification to solvable and unsolvable
+* Solvable Problems Classification
 === Teaching and Learning Methods - Evaluation ===
@@ Γραμμή 66: / Γραμμή 80: @@
 ! Delivery
 |
-ΧΧΧ
+Face to face
 |-
 ! Use of Information and Communications Technology
-|
+| Yes
-ΧΧΧ
 |-
 ! Teaching Methods
@@ Γραμμή 81: / Γραμμή 94: @@
 | 39
 |-
-| ΧΧΧ
+| Self study
-| 000
+| 78
 |-
-| ΧΧΧ
+| Exercises
-| 000
+| 70.5
 |-
 | Course total
@@ Γραμμή 93: / Γραμμή 106: @@
 ! Student Performance Evaluation
 |
-ΧΧΧ
+* Final essays (40%) .
+* Exercises -questions requiring critical thinking (30%).
+* Presentations of related issues (30%).
 |}
@@ Γραμμή 99: / Γραμμή 114: @@
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