西班牙语抑郁症与进食障碍的早期检测：UNSL 在 MentalRiskES 2023 中的工作
Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023
AI总结：
UNSL 团队在 MentalRiskES 2023 中提出基于 Transformer 并结合预测历史决策策略的模型，用于西班牙语进食障碍和抑郁症的早期检测，在任务 1 和任务 2 中均取得排名第二的性能。
摘要：
MentalRiskES 是一项新颖的挑战任务，旨在解决与西班牙语早期风险检测相关的问题。其目标是在考虑不同任务的情况下，尽快检测出表现出精神障碍迹象的 Telegram 用户。任务 1 涉及对用户进食障碍的检测，任务 2 侧重于抑郁症检测，任务 3 旨在检测一种未知障碍。这些任务被划分为若干子任务，每个子任务定义了一种解决思路。我们的研究团队参与了任务 1 和任务 2 的子任务 A：这是一个二分类问题，用于评估用户是阳性还是阴性。为解决这些任务，我们提出了基于 Transformer 的模型，并根据早期检测框架所定义的标准采用了一种决策策略。其中一个模型为每个待解决任务扩展了包含重要词语的词汇表。此外，我们应用了一种基于模型在用户评估过程中所做预测历史的决策策略。在任务 1 和任务 2 中，根据基于分类性能和延迟的排名，我们获得了第二好的表现，证明了我们的方法在解决西班牙语早期检测问题方面的有效性和一致性。
英文摘要：
MentalRiskES is a novel challenge that proposes to solve problems related to early risk detection for the Spanish language. The objective is to detect, as soon as possible, Telegram users who show signs of mental disorders considering different tasks. Task 1 involved the users' detection of eating disorders, Task 2 focused on depression detection, and Task 3 aimed at detecting an unknown disorder. These tasks were divided into subtasks, each one defining a resolution approach. Our research group participated in subtask A for Tasks 1 and 2: a binary classification problem that evaluated whether the users were positive or negative. To solve these tasks, we proposed models based on Transformers followed by a decision policy according to criteria defined by an early detection framework. One of the models presented an extended vocabulary with important words for each task to be solved. In addition, we applied a decision policy based on the history of predictions that the model performs during user evaluation. For Tasks 1 and 2, we obtained the second-best performance according to rankings based on classification and latency, demonstrating the effectiveness and consistency of our approaches for solving early detection problems in the Spanish language.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
