2019 projects
Objectives
- Define a study topic and an application in language technology. You may define them yourself or with the help of the instructor.
- Survey the relevant literature
- Implement an application prototype
- Evaluate it
- Write a project report in the form of a conference paper
- Submit this paper to a conference (optional). See for instance ACL 2020 .
Organization and Location
The project will take place in the 2nd LP. There is no dedicated location for it. The participants will work on the machines in the basement or on their own machines. The duration of time spent on the project should be of about two weeks. Each participant can work alone or collaborate with one or two other people.
A more complete description with possible subjects is available here.
Report and Programs
After your presentation, you will write a report of 4 to 8 pages that you will hand in together with your slides, and your programs (possibly through a public versioning repository). You will try to write your report as a research paper, like the ones you probably read when carrying your project out.
Please, use Latex and the Association for Computational Linguistics styles to compose your report so that we have a uniform presentation across all the papers. The styles are available here: https://acl2020.org/calls/papers/. Please use the A4 page size. Use also the Latex/Bibtex tool for your references. Should you have questions about it, please ask me. Görel Hedin wrote useful guidelines on how to write a report that you can read here.
When you are done with your project, please send me:
- the final report in PDF with the Latex sources. Do not paginate it;
- the slides in PDF, Powerpoint, OpenOffice, or similar formats; and
- the code in a zipped archive, possibly with a github link.
The deadline to hand in the report, the slides, and the code is Thursday January 16, 2020.
List of Projects
In total, there are 4 projects and 7 students. I wrote down the names of the students and the project titles as they came to me. They do not represent any commitment, but are just an indication. Students can change or modify the project title as they want.
- Berta Vinãs, Categorizing offensive language in the OffensEval corpus
- Rasmus Berggren and Dennis Londögård, Categorization of user reports to city services in Malmö
- Arvid Larsson, Recognition of guests and topics in podcasts
- Emil Aminy and Petter Berntsson, Identification of genes and proteins in scientific articles
- Malte Kauranen, Signature segmentation using single-shot detection
Schedule of Presentations
The project presentation consists of an oral description of your project and results that should be typically of 15 minutes followed by questions. There will be a beamer available in the room so that you can easily show your slides and demonstrations. Please read the presentation guidelines here before you give your talk: here. [ local copy].
Altogether, the presentation should not last more than 20 minutes. All the presentations will take place on December 18, 2019 in the E:2116 room. The table below shows the preliminary schedule.
In the presentation, you will shortly describe the background, your system (architecture and outline of your algorithms), and results. You should provide some kind of evaluation and ideally show a demonstration. Please bring your computer and have your slides on a USB stick.
You have other links and tips here: http://cs.lth.se/edan70-project-in-computer-science/projects-in-compilers/
Date | Name and Project title | Location |
Wednesdsay 18
15:00–15:20 | Berta Vinãs Categorizing offensive language in the OffensEval corpus | E:2116 |
Wednesdsay 18
15:20–15:40 | Rasmus Berggren and Dennis Londögård Categorization of user reports to city services in Malmö | E:2116 |
Wednesdsay 18
15:40–16:00 | Arvid Larsson Recognition of guests and topics in podcasts | E:2116 |
Wednesdsay 18
16:15–16:35 | Emil Aminy and Petter Berntsson Identification of genes and proteins in scientific articles | E:2116 |
Wednesdsay 18
16:35–16:55 | Malte Kauranen Signature segmentation using single-shot detection | E:2116 |