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 METE EMINAĞAOĞLU

Offered to

Computer Science
Ph.D. in 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, as well as speech processing and speech recognition. To establish knowledge in text processing, computational morphology, semantic and syntactic analysis, anaphora resolution, machine translation, information extraction, document classification and text classification, opinion mining, sentiment analysis, plagiarism detection, and spell checking and their implementations 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 convolutional and LSTM deep learning models for natural language processing, speech recognition, machine translation, and sentiment analysis.
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, speech processing and text 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 Quick review: Essentials of deep learning, machine learning, and artificial neural networks
2 Quick review: Essentials of computational linguistics, natural language processing
3 Intelligent speech processing Part 1: AI-based and machine learning based methodologies and models for speech recognition and text-to-speech processing
4 Intelligent speech processing Part 2: Deep learning approaches for speech recognition and text-to-speech processing
5 Advanced computational linguistics Part 1: AI-based and machine learning approaches for computational morphology, semantic and syntactic analysis, anaphora resolution
6 Advanced computational linguistics Part 2: Deep learning approaches for computational morphology, semantic and syntactic analysis, anaphora resolution
7 Advanced computational linguistics Part 3: AI, machine learning, and deep learning approaches for machine translation
8 Advanced computational linguistics Part 4: AI, machine learning, and deep learning approaches for word sense disambiguation, ambiguity resolution, and text generation
9 Advanced text processing Part 1: AI-based and machine learning approaches for sentiment analysis and opinion mining
10 Advanced text processing Part 2: Deep learning approaches for sentiment analysis and opinion mining
11 Advanced text processing Part 3: AI-based and machine learning approaches for information extraction, plagiarism detection, and spell checking
12 Advanced text processing Part 4: Deep learning approaches for information extraction, plagiarism detection, and spell checking
13 Advanced text processing Part 5: AI, machine learning, and deep learning approaches for document and text clustering
14 Project demos and presentations. General discussion and review of the topics covered throughout the term.

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 PRS PRESENTATION
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 +PRS * 0.20 +FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 +PRS * 0.20 +RST * 0.40


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

-

Assessment Criteria

-

Language of Instruction

English

Course Policies and Rules

-

Contact Details for the Lecturer(s)

mete.eminagaoglu@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
Final 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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