论文标题
使用文本搜索有监督的文本分类
Supervised Text Classification using Text Search
论文作者
论文摘要
监督文本分类是ML研究的经典和活跃领域。在大型企业中，解决此问题的解决方案具有重要意义。在票务系统中，这是特别正确的，在票务系统中，票务类型和亚型的预测给出了新传入的票务文本以找出最佳路由是一个数十亿美元的行业。 在本文中，作者描述了一类工业标准算法，这些算法可以准确（86 \％及以上）预测任何文本先前标记的文本数据的分类 - 通过新颖使用任何文本搜索引擎。 这些算法用于自动化与适当团队的发行票路由。这类算法对各种各样的工业应用，IT支持，RPA脚本触发，甚至法定领域都有巨大的后果，在这些领域中，已经可以使用大量的预先标记数据。
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 new incoming ticket text to find out optimal routing is a multi billion dollar industry. In this paper authors describe a class of industrial standard algorithms which can accurately ( 86\% and above ) predict classification of any text given prior labelled text data - by novel use of any text search engine. These 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.
