TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree With Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	-
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) Understand the foundational principles of computational linguistics. 2) Acquires the foundational methods used in computational linguistics. 3) Develops language processing applications and gains hands-on experience. 4) Knows how to apply the learned methods to a specific linguistic problem.
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	No other optional course are recommended.
COURSE DEFINITION	Introduction to Computational Linguistics, Language Models and N-grams, Named Entity Recognition and Relation Extraction, Text Classification and Methods, Markov Models and Part-of-Speech Tagging, Context-Free Grammar and Parsing, Parsing Methods, Semantics and Lambda Calculus, Lexical Semantics and Word Sense Disambiguation, Information Retrieval, Machine Translation, Recent Research and Applications
COURSE CONTENTS	WEEK TOPICS 1st Week Introduction to Computational Linguistics 2nd Week Language Models and N-grams 3rd Week Named Entity Recognition and Relation Extraction 4th Week Text Classification and Methods 5th Week Markov Models and Part-of-Speech Tagging 6th Week Context-Free Grammar and Parsing 7th Week Parsing Methods 8th Week Midterm 9th Week Semantics and Lambda Calculus 10th Week Lexical Semantics and Word Sense Disambiguation 11th Week Information Retrieval 12th Week Machine Translation 13th Week Recent Research and Applications 14th Week Recent Research and Applications
RECOMENDED OR REQUIRED READING	1. Speech and Language Processing, Jurafsky, D. & Martin, J. Prentice Hall, 2008. 2. Foundations of Statistical Natural Language Processing, Christopher D. Manning, and Hinrich Schutze. The MIT Press, 1999.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Questions/Answers,Project,Presentation,Report Preparation,Practice
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Quiz 1 15 Project 1 15 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 11 154 Preparation for Final exam 1 69 69 Course hours 14 3 42 Preparation for Midterm exam 1 44 44 Laboratory (including preparation) Final exam 1 2 2 Homework Total Workload 313 Total Workload / 30 10,43 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No