Natural Language Processing 2023: Project Ideas
The 2023 episode at Faculty of Mathematics, Physics and Informatics of Comenius University
This site lists various ideas that could be explored as possible projects as part of the NLP course. The list is far from exhaustive, but represents (current) course instructor('s/s) interest.
All of the ideas assume the final project will contain some "novelty bits", either in the explored idea itself, its execution, practical usability or the underlying dataset.
Creating a new dataset for any of the NLP tasks listed below (or any other, really) is a huge plus.
Table of Contents
KiniT in collaboration with Gerulata has trained a quite well performing Slovak BERT last year. The associated paper has tested it out on a few tasks but the standard approach with BERT-style models have been to test them on GLUE and SuperGLUE. Could we perhaps do that somehow?
Pick any interesting text dataset, the text of which can be classified into various categories and try a couple of methods.
Given a body of text (say a document or a news article), can we extract the most important phrases out of it (and hence help summarize it a bit)?
Various approaches do exist (many of the popular ones are implemented as part of the pke module) -- could you think of a dataset where something like this could be useful? Could we perhaps build one automatically (with the expected keywords attached)?
Can we build a Slovak/Czech/Croatian/Serbian/[any other language] summarization dataset that would allow us to "compress" news articles into a smaller list of sentences?
Can we use the TextRank algorithm to perform extractive summarization, or generate a list of keywords for a given body of text? Can you think of some interesting text for which this method would be a good fit?
Given words A and B, can we create their portmanteau C?
Or better yet, given the words A and B can we create a portmanteau C that would not directly feature A or B but would still be related? Such as for instance
|angry||Mozart||scaria (scary / aria)|
Here is a quick demo of such approach, which requires a pretty complex setup and external tools, such as the CMU Pronouncing Dictionary, which makes it pretty difficult to port this approach for different languages.
Is there something we can do to work around that? Some neural approach perhaps?
Feel free to come up with an idea on your own -- if you are working on something NLP-related for your thesis, that would be a good candidate. But in general, I'd be happy to talk about any NLP-related idea you may have.
Alternatively feel free to check out the sites below, find a NLP task you find interesting and see if you can make an interesting project out of it!