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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS ELECTIVE

Offered By

Computer Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÖZLEM VARLIKLAR

Offered to

Computer Engineering

Course Objective

The aim of this course is to provide the students to learn modelling natural languages.

Learning Outcomes of the Course Unit

1   Define morphological and statistical analysis 2   Differentiate the difference between working on small files and big files. 3   Collect big enough data to model the language 4   Construct a preliminary model for a natural language 5   Explain how ngram analysis is used in different areas

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Course Overview. Introduction. Brief History. 2 NLP Applications, Techniques, Linguistic Knowledge 3 Recognition as Search. Regular Expressions, Finite State Automata 4 Morphology & Finite State Transducers (Inflectional and Derivational Morphology) 5 Word Prediction, N-Grams. 6 Smoothing 7 Part-of-Speech (POS) Tagging, HMM, Entropy. 8 The Porter Stemmer and Human Morphological Processing, Ambiguity. 9 Probabilistic Models of Pronunciation and Spelling (Bayesian, Minimum Edit Distance(MED)) 10 The Porter Stemmer and Human Morphological Processing, Zipf's Law 11 Word Sense Disambiguation and Information Retrieval 12 Language Generation and Machine Translation 13 Project Presentations - I 14 Project Presentations - II

Recomended or Required Reading

Speech and Language Processing,Daniel Jurafsky,James H. Martin, Prentice Hall, 0131873210.

Planned Learning Activities and Teaching Methods

By using presentation tools in class, and giving programming assignments and project, all the students are expected to learn the modelling natural languages.

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

Course evaluation is done through homework, quizzes and written exams.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

email: aktas.ozlem@deu.edu.tr

Office Hours

Will be announced in first class.

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 2 28 Preparation for midterm exam 1 12 12 Preparation for final exam 1 16 16 Preparing assignments 1 16 16 Preparation for quiz etc. 4 2 8 Final 1 2 2 Midterm 1 2 2 Project Assignment 1 1 1 Quiz etc. 4 1 4 TOTAL WORKLOAD (hours) 145

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 LO.1 5 3 3 LO.2 3 LO.3 4 5 4 4 5 LO.4 3 3 LO.5 3 3 5 5 3