3 days, 1 hour from now

Second semester project for Data Mining 2021/2022 course

This is the second project for students enrolled in the Data Mining course 2021/2022 at the Faculty of Mathematics, Informatics, and Mechanics at the University of Warsaw. The task is to predict topics of scientific publications based on their abstracts.

Overview

The task in this challenge is to predict topics of scientific articles from the ACM Digital Library. The topics correspond to classes from the ACM Computing Classification System (the old version from 1998). This is a multi-label classification problem as each text can be assigned to multiple (at least one) classes.

More details regarding the available data, submission format, and evaluation can be found in the Task description section.

Terms & Conditions
 
 
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Rank Team Name Score Submission Date
1
jm394464
0.4566 2022-05-20 19:51:37
2
fm394471
0.4505 2022-05-20 20:50:56
3
m.procyk
0.4451 2022-05-22 22:06:21
4
baseline
0.4290 2022-05-3 15:46:43
5
wdrz
0.3572 2022-05-5 16:33:56
6
LK
0.2546 2022-05-24 21:04:45
7
abcd
0.1216 2022-05-23 15:56:01
8
jakubpw
0.0509 2022-05-26 19:03:45

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 (DM2022_training_docs_and_labels.tsv) 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 (DM2022_test_docs.tsv) 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.

Solutions will be evaluated on-line and the preliminary results will be published on the public leaderboard. The preliminary score will be computed on a small subset of the test time series (10%), fixed for all participants. The final evaluation will be performed after completion of the competition using the remaining part of the test data. Those results will also be published online. It is important to note that only teams that submit a report describing their approach before the end of the challenge will qualify for the final evaluation. Participants can submit many solutions but before the competition ends, each team needs to indicate up to two final solutions that will undergo the final evaluation (on the remaining part of the test data).

In case of additional questions, please post them on the competition forum. 

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There is no topics in this competition.