As a result of the growing production of unstructured textual data, techniques for representing words and documents in the vector space have emerged recently. The Brazilian Public Ministry has received several textual requests that are send by citizens with different needs, such as those involved in cases of domestic violence against women, others requesting intensive care unit admissions, and more. The time spent in classifying, detecting similar requests and distributing them is essential to optimize and save public resources. Therefore, we adopted the neural model with the Simulated Annealing (SA), a classic global optimization algorithm with low computational complexity, because of the need to reduce the daily training time, providing a more friendly graphic visualization of documents in high dimensions, supporting the judicial decision process. The physical analogy of the SA meta-heuristic associated with the continuous representation of documents in the vector space contribute greatly to the friendly visualization of a high-dimensional dataset, maintaining a comparable score with other deep models and optimization algorithms, such as Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Bayesian Optimization (BO).

由于非结构化文本数据的产生越来越多，最近出现了在向量空间中表示单词和文档的技术。巴西公共事务部收到了一些具有不同需求的公民发出的文本请求，例如那些涉及针对妇女的家庭暴力案件的请求，其他请求重症监护病房收治的请求等等。在分类，检测和分发相似请求上花费的时间对于优化和节省公共资源至关重要。因此，由于需要减少日常训练时间，因此我们采用了神经网络模型和模拟退火（SA）（一种经典的全局优化算法，具有较低的计算复杂度），从而可以更直观地显示高维文档，支持司法裁决程序。与向量空间中文档的连续表示相关联的SA元启发式方法的物理类比极大地有助于了高维数据集的友好可视化，并与其他深度模型和优化算法（例如协方差矩阵适应）保持了可比的分数进化策略（CMA-ES）和贝叶斯优化（BO）。