COURSE UNIT TITLE

: INTRODUCTION TO NATURAL LANGUAGE PROCESSING

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIL 4129 INTRODUCTION TO NATURAL LANGUAGE PROCESSING ELECTIVE 2 2 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR KADRIYE FILIZ BALBAL

Offered to

Computer Science

Course Objective

The aim of this course is to have knowledge about the development and basic algorithms of natural language processing and to gain the ability to practice using natural language processing techniques.

Learning Outcomes of the Course Unit

1   To have knowledge about the definition of natural language processing,
2   To be able to comprehend basic natural language processing algorithms,
3   To be able to determine the appropriate natural language processing techniques for the problem,
4   To be able to interpret by applying natural language processing methods.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Natural Language Processing
2 Fundamentals of Linguistics and Language Models
3 Syntactic Analysis And Morphological Analysis
4 Introduction to Machine Learning
5 Regular Expressions
6 Finding an Asset Name
7 Text Classification
8 String Algorithms
9 Hidden Markov Models and Applications
10 Information Extraction
11 Text Indexing and Access
12 Machine Translation
13 Project Presentations
14 Project Presentations

Recomended or Required Reading

Textbook(s): Natural Language Understanding, J.Allen, Benjamin-Cummings.
Supplementary Book(s):
+ Speech and Language Processing, Jurafsky and Martin, Prentice Hall
+ Foundations of Statistical Natural Language Processing, C. D. Manning, H. Schütze, MIT
+ Handbook of Natural Language Processing, R. Dale, H. Moisl, H.Somers,Marcel Dekker.

Planned Learning Activities and Teaching Methods

The course will continue in the form of lectures, homework presentations and discussion by enriching the theoretical content with applications. In addition to the lesson taught, presentations will be prepared in groups and presented in the form of discussion sessions. In some weeks of the course, the results of the previous homework will be discussed and reinforced.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.30 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.30 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

Exams, assignments

Language of Instruction

Turkish

Course Policies and Rules

Homework submission must be done on time.

Contact Details for the Lecturer(s)

kadriyefiliz.balbal@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 8 8
Preparation for final exam 1 10 10
Preparing assignments 1 6 6
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 126

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.155555
LO.25455
LO.355555
LO.454555