Supervised text classification is a classical and active area of ML research. In large enterprise, solutions to this problem has significant importance. This is specifically true in ticketing systems where prediction of the type and subtype of tickets given text to optimal routing is a multi billion dollar industry. By novel use of any text search engine the authors stumbled upon an industrial algorithm class which can accurately ( 86% and above ) predict classification of any text given prior labelled text data. This algorithms were used to automate routing of issue tickets to the appropriate team. This class of algorithms has far reaching consequences for a wide variety of industrial applications, IT support, RPA script triggering, even legal domain where massive set of pre labelled data are already available.

翻译：监管文本分类是ML研究的经典和活跃领域。 在大型企业中,解决这个问题的解决方案非常重要。 在票单系统中,预测给付最佳路线的文本的类型和亚型是一个数十亿美元的行业。 使用任何文本搜索引擎,作者会偶然发现一个工业算法类(86%及以上)可以准确预测先前标签的文本数据的任何文本的分类。 这种算法被用来自动将发行票的路径向适当的团队进行。 这种算法对于广泛的工业应用、信息技术支持、触发 RPA 脚本(甚至法律领域) 具有深远的影响,因为已经存在大量的预贴有标签的数据。