Syllabus of CS 483 - Natural Language Processing


Department: Computer Engineering

Credits: Bilkent 3,    ECTS 5

Course Coordinator: Varol Akman

Semester: 2020-2021 Spring

Contact Hours: 3 hours of lecture per week   
 
Textbook and Other Required Material:
  • Recommended - Textbook: Speech and Language Processing (3rd ed. draft), Dan Jurafsky and James H. Martin [download]
 
Catalog Description:
History of natural language processing (NLP). The provenance of analysis and transformation of language by computational techniques. General linguistic preliminaries. Representations of text and speech that can aid prediction, extraction, and semantic reasoning over language. Automatic mining of knowledge from the web. The discipline of machine learning and its significance for NLP. Deep learning as a fundamental method for NLP. Recent technological developments in NLP, including automatic language translators such as Google Translate and personal assistants such as Siri.
 
Prerequisite(s): (CS101 or CS115) and CS315 and MATH132 and (MATH225 or MATH220 or MATH241)
 
Assessment Methods:
  Type Label Count Total Contribution
1 In-class attendance Miss no more than 9 lectures in order to receive 10%. Otherwise, you get 0%. 1 10
2 Portfolio A blog of ≈ 250 wd. reviews of 10 papers relevant to term project. Groups of up to 3 students. 1 20
3 Midterm Closed-book, closed-notes. 1 10
4 Term project Proposal (10%). 2 demos (5% ea.) Code (10%) due last week of classes. Groups of up to 3 students. 1 30
5 Term essay ≈ 2500 wd. Theories underlying term project. Groups of up to 3 students. Due last week of classes. 1 20
6 Final Closed-book, closed-notes. 1 10
 
Minimum Requirements to Qualify for the Final Exam:
Collect at least 12% out of 40% [which is the sum of Proposal (10%), 2 demos (5% ea.) , and Midterm (10%)]
 
Course Learning Outcomes:
Course Learning Outcome Assessment
 
Weekly Syllabus:
  1. Regular Expressions, Text Normalization, and Edit Distance
  2. Finite State Transducers, Spelling Correction
  3. Neural Nets and Neural Language Models
  4. Hidden Markov Models
  5. Part-of-Speech Tagging, Formal Grammars of English
  6. Syntactic Parsing, Statistical Parsing, Dependency Parsing
  7. Vector Semantics, Semantics with Dense Vectors
  8. Lexicons for Sentiment and Affect Extraction
  9. Computational Semantics, Information Extraction
  10. Coreference Resolution, Discourse Coherence
  11. Summarization
  12. Machine Translation
  13. Question Answering, Conversational Agents
  14. Speech Recognition/Speech Synthesis
 
ECTS - Workload Table:
Activities Number Hours Workload
Total Workload: 0
Total Workload / 30: 0 / 30
  0
ECTS Credits of the Course: 5
 
Type of Course:   Lecture
 
Course Material:   Lecture Notes - Slides
 
Teaching Methods:   Lecturing - Independent study