729A97 Language Engineering Systems / 2016
Introduction
Marco Kuhlmann
Department of Computer and Information Science
What will you do in this course?
• survey a research field within natural language processing
topic: universal dependencies
• plan, implement and evaluate a project
not necessarily on universal dependencies!
• write scientific papers
on universal dependencies, on your project work
• get acquainted with standard practices in the field
reviewing, where to find literature, form of a project proposal
Universal Dependencies
• framework for grammatical annotation
• transparent and consistent across languages
• work in progress for 28 languages
• wide range of interesting applications
multilingual parsing research and development, cross-lingual learning and
annotation projection, empirical language typology
Structure of the course
• Reading group (50 hours)
• reading research articles (16 hours)
• seminars (6 hours)
• literature survey (28 hours)
• Individual project (106 hours)
• project work (80 hours)
• seminars (14 hours)
• project presentation and report (12 hours)
Tuesday 13–15
W4
Tuesday 15–17
Wednesday 10–12
26/1. Lecture: Introduction.
Dependency Grammar
27/1. Lecture: Dependency
Parsing. NLP R&D
2/2. Seminar:
Research Papers
3/2. Seminar:
Research Papers
W5
2/2. Seminar:
Research Papers
W6
Read background literature. Find a project.
W7
16/2. Seminar:
Project Proposals
W8
Project work
W9
Project work
W10
Project work
W11
Project work. Prepare the final report.
W12
22/3. Seminar:
Final Report
W13
Finish the project. Write the final report.
16/2. Seminar:
Project Proposals
17/2. Seminar:
Project Proposals
8/3. Seminar:
Progress Report
Project work
22/3. Seminar:
Final Report
23/3. Seminar:
Final Report
Intended learning outcomes
By the time you finish the course, you should be able to:
• describe and assess the content of scientific articles within natural
language processing;
reading group
• give an account of methods and applications in the area of natural
language processing;
reading group, project
Intended learning outcomes
By the time you finish the course, you should be able to:
• show an increased ability to independently analyse and solve
problems within natural language processing;
project
• to specify and implement a larger NLP component based on
methods and algorithms from the literature;
project
Intended learning outcomes
By the time you finish the course, you should be able to:
• apply methods to create NLP data and models, including methods
that build on machine learning from text (or speech);
project
• assess what criteria and measures are relevant for the evaluation of
different NLP components and systems and independently carry
out an evaluation.
project