  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.
