Project for an academic contest on http://knowledgepit.ml, which is also the second project for Data Mining 2019/2020 course at University of Warsaw.
Data for this project consists of two tables in a tab-separated columns format. Each row in those files corresponds to an abstract of a scientific article from ACM Digital Library, which was assigned to one or more topics from the ACM Computing Classification System.
The training data (DM2020_training_docs_and_labels.csv) has three columns: the first one is an identifier of a document, the second one stores the text of the abstract, and the third one contains a list of comma-separated topic labels.
The test data (DM2020_test_docs.csv) has a similar format, but the labels in the third column are missing.
The task and the format of submissions: the task for you is to predict the labels of documents from the test data and submit them to the evaluation system. A correctly formatted submission should be a text file with exactly 100000 lines. Each line should correspond to a document from the test data set (the order matters!) and contain a list of one or more predicted labels, separated by commas.
Evaluation: the quality of submissions will be evaluated using the average F1-score measure, i.e., for each test document, the F1-score between the predicted and true labels will be computed, and the values obtained for all test cases will be averaged.
The solution with full description is in Jupyter Notebook abstracts.ipynb (html version abstracts.html).
virtualenv --python=python3 venv
source venv/bin/activate
pip install -r requirements.txt
jupyter labPreliminary score on the partial dataset was 0.4296.
After final evaluation the score was 0.429108, which gave me the third place in the
contest (second place excluding the organizer)!