IN2110 – Methods in Language Technology
Changes in the course due to coronavirus
Autumn 2020 the exams of most courses at the MN Faculty will be conducted as digital home exams or oral exams, using the normal grading scale. The semester page for your course will be updated with any changes in the form of examination.
This course gives an in-depth study in basic methods and practical tools for basal language technology (methods for automatic analyzation of language-based data). It covers both rule-based techniques, such as phrase structure grammar, and approximations with a starting point in machine learning, such as vector space semantics and classification. The course will take a look at some applications of methods for issues within language technology such as tagging, parsing and text classification (such as sentiment analysis). The course has a strong practical component, with use of relevant tools and projects with written rapports, among other things, which is needed to qualify for the exam.
When you´re done with this course, you´ll:
- have mastered basic techniques for analyzing linguistic data
- have knowledge of vectorspace semantics and its applications
- be able to explain and process phase structure- and dependence grammar
- have insight in basal techniques used for language technological experimentation
- be able to independently finish a project using standard tools
- have training in how to write a scientific summary of a project
- be able to analyse an issue from a interdisciplinary perspective
- be able to combine techniques from computer science, linguistics, machine learning and more
Admission to the course
Students at UiO register for courses and exams in Studentweb.
Special admission requirements
In addition to fulfilling the Higher Education Entrance Qualification, applicants have to meet the following special admission requirements:
- Mathematics R1 or Mathematics (S1+S2)
The special admission requirements may also be covered by equivalent studies from Norwegian upper secondary school or by other equivalent studies. Read more about special admission requirements (in Norwegian).
Formal prerequisite knowledge
Recommended previous knowledge
- 5 credits overlap with INF2820 – Computational linguistics (continued).
4 hours a week: 2 hours of lectures and 2 hours in groups.
Submission and approval of mandatory assignments are required. Read more about mandatory assignments and other hand-ins. Attending of the first lecture is compulsory.
4 hours written digital exam. All mandatory assignments must be passed to be allowed to take the exam.
Examination support material
No examination support material is allowed.
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. 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.