Admission requirements
None.
Description
In this course, students will learn to apply a variety of machine learning models to the analysis of natural language. We will be using off-the-shelf machine learning toolkits, such as NLTK, LingPipe, Pattern and Spacy. Topics range from senrtiment analysis to part of speech tagging, syntaxtic analysis (parsing), morphology and document analysis (topic labeling).
The course consists of 12 lectures and home or in-class assignments. In the lectures, we will discuss the formal background of machine learning-based natural language processing, and recent relevant literature. In the assignments, students will use an online environment (Google Colab) in which they can run their experiments. Code will be provided by the teacher; students learn to adapt the code and run their own experiments.
Course objectives
Students will build up practical skills for applying (and understanding) machine learning-based approsaches to the analysis of language. This course will help build the fundamentals for research-oriented classes in the MA track Computational Linguistics. Students will acquire analytical skills that help them to interpret algorithm outcomes. They will learn to work together, and report on their findings.
Timetable
The timetables are available through My Timetable.
Mode of instruction
Lectures
Assessment method
Assessment
Oral group presentation and written exam with open and essay questions.
Students will be evaluated through (1) a written exam (2) a joint presentation of a practical group assignment consisting of an implementation of an NLP topic (here, quality of solutions is not important; just a clear experimental narrative). Topics for the group assignment are voluntary, and will be chosen around 2/3 of the course, so that students can get timely assistance.
Weighing
The final grade will consist of an unweighted average of the scores for the written exam and the practical assignment.
Resit
The resit for the written exam will consist of an alternative partial written exam readdressing the topics of the failed questions. The resit for the group assignment will consist of a reprisal of the group presentation.
Inspection and feedback
How and when an exam review will take place will be disclosed together with the publication of the exam results at the latest. If a student requests a review within 30 days after publication of the exam results, an exam review will have to be organized.
Reading list
Stephan Raaijmakers: Deep learning for Natural Language Processing. Manning, 2022 (optional)
Literature (papers), to be distributed during the course.
Python: http://www.spronck.net/pythonbook/index.xhtml
Registration
Enrolment through MyStudyMap is mandatory.
General information about course and exam enrolment is available on the website.
Registration Exchange
For the registration of exchange students contact Humanities International Office.
Contact
For substantive questions, contact the lecturer listed in the right information bar.
For questions about enrolment, admission, etc, contact the Education Administration Office: Reuvensplaats
Remarks
None.