SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing

• URL: http://arxiv.org/abs/2509.14797v1
• Date: Thu, 18 Sep 2025 09:50:14 GMT
• Title: SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
• Authors: Alba Maria Marmol-Romero, Flor Miriam Plaza-del-Arco, Arturo Montejo-Raez,
• Abstract summary: This paper describes the participation of the SINAI team in the eRisk@CLEF lab.<n>One of the proposed tasks has been addressed: Task 2 on the early detection of signs of pathological gambling.<n>The approach presented in Task 2 is based on pre-trained models from Transformers architecture with comprehensive preprocessing data and data balancing techniques.
• Score: 3.987649624343527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, one of the proposed tasks has been addressed: Task 2 on the early detection of signs of pathological gambling. The approach presented in Task 2 is based on pre-trained models from Transformers architecture with comprehensive preprocessing data and data balancing techniques. Moreover, we integrate Long-short Term Memory (LSTM) architecture with automodels from Transformers. In this Task, our team has been ranked in seventh position, with an F1 score of 0.126, out of 49 participant submissions and achieves the highest values in recall metrics and metrics related to early detection.

Related papers

• NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training [6.030518150035875]
  This paper introduces a two-stage alignment strategy for EEG foundation models.<n>First, we propose NeuroTTT: a domain-specific self-supervised fine-tuning paradigm.<n>Second, we perform self-supervised test-time training on individual unlabeled test samples.<n>Our approach is the first to unify domain-tuned self-supervision with test-time training in large-scale EEG foundation models.
  arXiv  Detail & Related papers  (2025-09-30T14:14:46Z)
• SINAI at eRisk@CLEF 2022: Approaching Early Detection of Gambling and Eating Disorders with Natural Language Processing [3.303345937552717]
  This paper describes the participation of the SINAI team in the eRisk@CLEF lab.<n>The approach presented in Task 1 is based on the use of sentence embeddings from Transformers.<n>The approach for Task 3 is based on text similarity estimation using contextualized word embeddings from Transformers.
  arXiv  Detail & Related papers  (2025-09-18T10:03:31Z)
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining [73.81862342673894]
  Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. We conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. Our models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection.
  arXiv  Detail & Related papers  (2024-03-20T09:17:22Z)
• Mavericks at ArAIEval Shared Task: Towards a Safer Digital Space -- Transformer Ensemble Models Tackling Deception and Persuasion [0.0]
  We present our approaches for task 1-A and task 2-A of the shared task which focus on persuasion technique detection and disinformation detection respectively. The tasks use multigenre snippets of tweets and news articles for the given binary classification problem. We achieved a micro F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task 2-A (7th rank on the leaderboard) respectively.
  arXiv  Detail & Related papers  (2023-11-30T17:26:57Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining [0.0]
  This paper describes our system on SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) We propose a set of transformer-based pre-trained models with task-adaptive pretraining and ensemble learning. On the test dataset, our system achieves F1-scores of 83%, 64%, and 47% on subtasks A, B, and C, respectively.
  arXiv  Detail & Related papers  (2023-05-11T15:29:04Z)
• Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences [49.91741677556553]
  We propose TempO, a temporal ordering pretext task for pre-training region-level feature representations for perception tasks. We embed each frame by an unordered set of proposal feature vectors, a representation that is natural for object detection or tracking systems. Extensive evaluations on the BDD100K, nuImages, and MOT17 datasets show that our TempO pre-training approach outperforms single-frame self-supervised learning methods.
  arXiv  Detail & Related papers  (2023-02-17T18:18:27Z)
• RoBLEURT Submission for the WMT2021 Metrics Task [72.26898579202076]
  We present our submission to the Shared Metrics Task: RoBLEURT. Our model reaches state-of-the-art correlations with the WMT 2020 human annotations upon 8 out of 10 to-English language pairs.
  arXiv  Detail & Related papers  (2022-04-28T08:49:40Z)
• Data Processing Matters: SRPH-Konvergen AI's Machine Translation System for WMT'21 [0.0]
  We describe the submission of the joint Samsung Research Philippines-Konvergen AI team for the WMT'21 Large Scale Multilingual Translation Task - Small Track 2. We submit a standard Seq2Seq Transformer model to the shared task, relying mainly on the strength of our data preprocessing techniques to boost performance. Our model ranked first in Indonesian to Javanese, showing that data preprocessing matters equally, if not more, than cutting edge model architectures and training techniques.
  arXiv  Detail & Related papers  (2021-11-20T03:56:37Z)
• Domain-matched Pre-training Tasks for Dense Retrieval [68.07140087626637]
  Pre-training on larger datasets with ever increasing model size is now a proven recipe for increased performance across almost all NLP tasks. We show that, with the right pre-training setup, this barrier can be overcome. We demonstrate this by pre-training large bi-encoder models on 1) a recently released set of 65 million synthetically generated questions, and 2) 200 million post-comment pairs from a preexisting dataset of Reddit conversations.
  arXiv  Detail & Related papers  (2021-07-28T19:13:00Z)
• Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID Twitter BERT and Bagging Ensemble Technique based on Plurality Voting [0.0]
  We develop a system that automatically identifies whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. Our final approach achieved an F1-score of 0.9037 and we were ranked sixth overall with F1-score as the evaluation criteria.
  arXiv  Detail & Related papers  (2020-10-01T10:54:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.