Abstract
This paper describes a novel approach based on Approximate Nearest Neighbors (ANN) techniques for modifying the granularity of the label schema in the training dataset of a classification task, from a user-based annotation to a message-based one. In particular, we tackle Task 1 of the CLEF 2022 eRisk Workshop which consists in the processing of messages written by Social Media users, in order to detect early signs of pathological gambling. Our proposal is based on the calculation of the nearest neighbors of the vectorial representations of the given messages, originally annotated at user-level. This way, we obtain a re-labeled training dataset in which messages from the same user can be either positive or negative. We then use this re-labeled dataset for performing the final classification on test instances. Compared to other systems participating in the task, our approach achieves the best average performance in the proposed evaluation frameworks, and shows to be the fastest one in terms of time needed to process the whole test dataset. This indicates that the proposed relabeling scheme allows us to capture more easily the textual information that leads to a correct detection of pathological gambling.
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Acknowledgments
This work has been partially supported by the Spanish Ministry of Science and Innovation within the DOTT-HEALTH Project (MCI/AEI/FEDER, UE) under Grant PID2019-106942RB-C32 and OBSER-MENH Project (MCIN/AEI/10.13039/501100011033 and NextGenerationEU/PRTR) under Grant TED2021-130398B-C21 as well as project RAICES (IMIENS 2022) and the research network AEI RED2018-102312-T (IA-Biomed).
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Fabregat, H., Duque, A., Araujo, L., Martinez-Romo, J. (2023). A Re-labeling Approach Based on Approximate Nearest Neighbors for Identifying Gambling Disorders in Social Media. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham. https://doi.org/10.1007/978-3-031-42448-9_15
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