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Information
| Code | CENG017 |
| Name | Computational Linguistic |
| Term | 2023-2024 Academic Year |
| Term | Fall |
| 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 | Prof. Dr. UMUT ORHAN |
| Course Instructor |
1 |
Course Goal / Objective
In this course, the main goal is to define the methods and approaches used in Computational Linguistics.
Course Content
Normalization, Tokenizing, Lemmatization, Parsing, Syntax, N-Gram analysis, Features and Analysis, Part of Speech Tagging, Treebank, Semantic analysis, Morphological and Semantic Ambiguity, Lexical Similarity, Semantic Similarity, Dialogue Systems, Question Answering, Machine Translation, Keyword Extraction, Document Summarization, Paraphrasing, Ontology Mapping
Course Precondition
none
Resources
Daniel Jurafsky and James H. Martin, Speech and language processing an introduction to natural language processing, computational linguistics, and speech, 2000.
Notes
Articles
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Knows basic NLP terms |
| LO02 | Does basic text operations |
| LO03 | Knows semantical and morphological analysis and ambiguation |
| LO04 | Knows lexical and semantic based similarity |
| LO05 | Knows Dialogue Systems and Question Answering |
| LO06 | Knows basics of Machine Translation |
| LO07 | Does a basic Keyword Extraction and Document Summarization application |
| LO08 | Knows Paraphrasing and Ontology Mapping |
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. | 4 |
| 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. | |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 3 |
| 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. | 3 |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 2 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 3 |
| 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. | 2 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. |
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: Ödev, Proje / Tasarım |
| 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 | Review for final exam | Study to lecture notes and applications | Öğretim Yöntemleri: Soru-Cevap, Problem Çözme |
| 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 | ||