IN9550 – Neural Methods in Natural Language Processing
Changes in the course due to coronavirus
Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.
Spring 2020: Teaching and examinations was digitilized. See changes and common guidelines for exams at the MN faculty spring 2020.
This course studies a selection of advanced techniques in Natural Language Processing (NLP), with particular emphasis on recent and current research literature. The focus will be on machine learning and specifically ‘deep’ neural network approaches to the automated analysis of natural language text. Topics will typically include representation learning for words (and possibly larger linguistic units), classification using Convolutional Neural Networks, and applications of various types of Recurrent Neural Networks to sequence labeling and the analysis of grammatical or semantic structure. The course includes strong practical components and puts emphasis on NLP problems and (potentially large) datasets of central importance in current research. Thus, students will be prepared to pursue an experimental, research-oriented MSc project in Natural Language Processing.
Upon completion of this course you:
- are familiar with common techniques for learning dense representations (‘embeddings’) of natural language;
- understand the basics of various types of neural networks and their applications to natural language processing;
- can apply off-the-shelf NLP tools in meaningful ways to the data preparation for representation learning;
- have basic knowledge of the concepts of transfer and multi-task learning in application to natural language problems;
- can design, excecute, analyze, and summarize large-scale experiments in common neural network toolkits;
- know how to assess the benefits and challenges of neural learning in contrast to other common approaches in NLP;
- are able to identify and critically read relevant NLP research literature;
- have the ability to conduct in-depth error analysis of experimental results and make corresponding design adjustments.
Admission to the course
PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.
If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.
PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.
- 10 credits overlap with IN5550 – Neural Methods in Natural Language Processing.
- 5 credits overlap with INF5820 – Language technological applications (discontinued).
- 5 credits overlap with INF9820 – Language technological applications (discontinued).
Four hours of instruction per week, mostly split into two hours of lectures and another two hours with hands-on (computer) laboratory work.
Mandatory assignments must be approved in order to qualify for the final exam. Previously approved assignments remain valid for one year.
An oral research presentation during the semester and home exam (a practical project and summary report). Both the presentation and the home exam must be passed, and both must be passed in the same semester.
It will also be counted as one of your three attempts to sit the exam for this course, if you sit the exam for one of the following courses: IN5550 - Advanced Topics in Natural Language Processing
Grades are awarded on a pass/fail scale. Read more about the grading system.
Resit an examination
Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester. Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.