CENG0044 Advanced Topics in Natural Language Processing

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG0044
Name Advanced Topics in Natural Language Processing
Term 2024-2025 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator


Course Goal / Objective

The aim of the course is to explain the techniques and methods of Natural Language Processing (DDI) theoretically, to explain the techniques and methods used in the background of sentiment analysis, topic detection, keyword extraction, question-answer system design, chat-bot design and language converter software.

Course Content

Introduction to Natural Language Processing, Normalization, Lemmatization, Parsing, POS, Syntax, N-gram, Corpus (Properties and Analysis), Part of Speech (POS) Labeling, Simple Semantic Analysis, Morphological and Semantic Ambiguity, Lexical Similarity, Semantic Similarity, Dialogue Systems and Question Answering, Machine Translation, Keyword Extraction-Document Summarization, Interpretation / Ontology Mapping

Course Precondition

none

Resources

Natural Language Processing with Python Written By Steven Bird, Ewan Klein and Edward Loper Statistical Machine Translation Written By Philipp Koehn Foundations of Statistical Natural Language Processing By: Christopher D Manning & Hinrich Schutze

Notes

Some papers


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Understands the concept of natural language processing
LO02 Understands the concept of vector space, comments difference between morphological and semantic similarities
LO03 Understands question and answer systems and methods used
LO04 Learns the concept and methods of machine translation


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. 2
PLO02 Bilgi - Kuramsal, Olgusal By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. 3
PLO03 Yetkinlikler - Öğrenme Yetkinliği Being aware of the new and developing practices of his / her profession and examining and learning when necessary. 2
PLO04 Yetkinlikler - Öğrenme Yetkinliği Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. 4
PLO05 Yetkinlikler - Öğrenme Yetkinliği Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process.
PLO06 Yetkinlikler - Öğrenme Yetkinliği Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. 3
PLO07 Beceriler - Bilişsel, Uygulamalı Has the skills of learning. 4
PLO08 Beceriler - Bilişsel, Uygulamalı Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. 2
PLO09 Beceriler - Bilişsel, Uygulamalı Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering.
PLO10 Beceriler - Bilişsel, Uygulamalı Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. 2
PLO11 Beceriler - Bilişsel, Uygulamalı Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. 3
PLO12 Bilgi - Kuramsal, Olgusal Observes social, scientific and ethical values in all professional activities. 1


Week Plan

Week Topic Preparation Methods
1 Introduction to NLP concept and terms Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
2 Normalization, Tokenizing, Lemmatization, Parsing Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
3 Syntax, N-Gram analysis Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
4 Corpus: Features and Analysis Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
5 Part of Speech Tagging, Treebank Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
6 Semantic analysis (probabilistic methods) Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
7 Morphological and Semantic Ambiguity Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
8 Mid-Term Exam Study to all lecture notes Ölçme Yöntemleri:
Yazılı Sınav
9 Lexical Similarity Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
10 Semantic Similarity Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
11 Dialogue Systems, Question Answering Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
12 Machine Translation Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
13 Keyword Extraction, Document Summarization Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
14 Paraphrasing, Ontology Mapping Study to lecture notes and applications Öğretim Yöntemleri:
Anlatım
15 Projects Study to lecture notes and applications Ölçme Yöntemleri:
Proje / Tasarım
16 Term Exams Study to all lecture notes Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Study to all lecture notes Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 24.05.2024 04:47