COURSE UNIT TITLE

: SPECIAL TOPICS IN COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSC 5027 SPECIAL TOPICS IN COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

ASSISTANT PROFESSOR KADRIYE FILIZ BALBAL

Offered to

Ph.D. in Computer Science (English)
Computer Science

Course Objective

To provide the students with a comprehensive theoretical and applied study of special and advanced topics in; computational linguistics, natural language processing, human language technologies, natural-language human-computer interaction. To establish knowledge in text processing, computational morphology, semantic and syntactic analysis, anaphora resolution, information extraction, document classification and text classification, opinion mining and sentiment analysis with several methodologies and models such as artificial intelligence, machine learning, artificial neural networks, deep learning.

Learning Outcomes of the Course Unit

1   Implement and develop smart systems and solutions for linguistic problems in companies or institutions.
2   Design and implement deep learning models for natural language processing.
3   Design and implement artificial intelligence and machine learning models for computational linguistics.
4   Plan, manage and use different advanced methodologies, procedures and techniques in natural language processing.
5   Develop or implement research projects in the area of computational linguistics and text mining.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Essentials of deep learning, machine learning, and artificial neural networks
2 Essentials of deep learning, machine learning, and artificial neural networks
3 Essentials of computational linguistics, natural language processing
4 Deep learning approaches for computational morphology, semantic and syntactic analysis, anaphora resolution
5 AI, machine learning, and deep learning approaches for word sense disambiguation, ambiguity resolution, and text generation
6 AI, machine learning, and deep learning approaches for word sense disambiguation, ambiguity resolution, and text generation
7 Deep learning approaches for sentiment analysis and opinion mining
8 Deep learning approaches for sentiment analysis and opinion mining
9 Deep learning approaches for for information extraction
10 Deep learning approaches for for information extraction
11 Deep learning approaches for document and text clustering
12 Deep learning approaches for document and text clustering
13 Project demos and presentations
14 Project demos and presentations

Recomended or Required Reading

D. Jurafsky and J. H. Martin, Speech and Language Processing, Prentice Hall, 3rd edition, 2018.

Y. Bengio, I. Goodfellow and A. Courville, Deep Learning, MIT Press, 2016.

T. Reamy, Deep Text: Using Text Analytics to Conquer Information Overload, Get Real Value from Social Media, and Add Bigger Text to Big Data, Information Today Inc., 2016.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 RAS RESEARCH ASSIGNMENT
4 PAR PARTICIPATION
5 FCG FINAL COURSE GRADE MTE* 0.20 + ASG * 0.20 +RAS * 0.50 + PAR * 0.10


Further Notes About Assessment Methods

-

Assessment Criteria

-

Language of Instruction

English

Course Policies and Rules

-

Contact Details for the Lecturer(s)

kadriyefiliz.balbal@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 13 4 52
Preparation for final exam 1 24 24
Preparing assignments 2 30 60
Preparing presentations 2 15 30
Midterm 1 2 2
TOTAL WORKLOAD (hours) 210

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15555
LO.25555
LO.35555
LO.45555
LO.55555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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