CENG784 STATISTICAL METHODS IN NATURAL LANGUAGE PROCESSING
| Course Code: | 5710784 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| Department: | Computer Engineering |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Lecturer Dr. RUKET ÇAKICI |
| Offered Semester: | Fall and Spring Semesters. |
Course Objectives
By the end of the course
Student will have a basis for Statistical Natural Language Processing concepts and current applications of Computational Linguistics .
Student will be able to present applicable linguistic theory by turning theories into practical techniques with emphasis on problems for which there are widely accepted solutions.
Student will be able to recognize the stateof-the-art research directions on computational linguistics and language technology.
Course Content
Statistical and Data Intensive methods for natural language processing. Language models, statistical machine translation. Information retrieval, text categorisation. Word sense disambiguation.
Course Learning Outcomes
By the end of the course student will be able to:
Describe the basic concepts in natural language processing
Create language models and use them as components of different NLP applications
Identify problems in natural language processing and design probabilistic models to solve them.
Understand the fundamentals of first order hidden markov models and the types of problems to which they are used as a solution.
Understand the fundamentals of different syntactic parsing systems/environments
Design and Build natural language processing systems consisting of different modules for processing different levels of information such as POS tagging, syntax, discourse etc
Present a state of the art research paper enriched with relevant literature survey
Communicate a research proposal through a technical document
Present and demonstrate an NLP project
Write a research paper with the results of the term project.
Program Outcomes Matrix
| Contribution | |||||
| # | Program Outcomes | No | Yes | ||
| 1 | Competence in fundamental and advanced knowledge of hardware and software Proficiency in problem solving. | ✔ | |||
| 2 | The ability to follow the contemporary technical development, and Initiative and aptitude for self-directed learning. | ✔ | |||
| 3 | They are capable of designing, and conducting experiments at advanced level. | ✔ | |||
| 4 | The ability to design and implement systems involving hardware, software, and the interaction between the two through challenging projects. | ✔ | |||
| 5 | Analyze and compare relative merits of alternative software design, algorithmic approaches and computer system organization, with respect to a variety of criteria relevant to the task (e. g. efficiency, scalability, security). | ✔ | |||
| 6 | Strong interpersonal skills needed for working effectively in small, diverse groups on medium to large scale technical projects. | ✔ | |||
| 7 | Strong oral communication skills essential for effectively presenting technical material to an audience and strong written communication skills and the ability to write technical documents that include specification, design, and implementation of a major project. | ✔ | |||