This document provides all details needed to have access to the research collection of "BDI-Sen: A Sentence Dataset for Clinical Symptoms of Depression".

Any scientific publication derived from the use of this collection should explicitly refer to the following publication:

Anxo Pérez, Javier Parapar, Álvaro Barreiro, and Silvia López-Larrosa. 2023. BDI-Sen: A Sentence Dataset for Clinical Symptoms of Depression. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23), July 23–27, 2023, Taipei, Taiwan. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3539618. 3591905

The BDI-Sen collection are available for research purposes under proper user agreements.

					train-with-severities-and-multilabels.csv
					val-with-severities-and-multilabels.csv
					test-with-severities-and-multilabels.csv
				

The columns are (Sentence, Label). It includes all the symptoms considered from the BDI-II and if it is relevant for each one.
The excel files (.xlsx), correspond to the original excel files used by the annotators. We used them to append all the symptom severities together.

***Important (balance of the labels):***

(a) In the case of symptom detection, we balanced the labels in train/val splits to have the same number of relevants (1) and non-relevants (0).
(b) In the case of severity detection, the number of non-relevants in train/val splits are less. In this case, we included as many samples from the control class (4) as the max number of training samples from the remaining other classes (0,1,2,3).