Supervised Text Classification using Text Search

TL;DR

This paper introduces a novel approach to supervised text classification by leveraging text search engines, achieving high accuracy and enabling automation in various industrial applications.

Contribution

The authors propose a new class of algorithms that use text search engines for supervised classification, demonstrating high accuracy and broad industrial applicability.

Findings

Achieved over 86% accuracy in text classification.

Applied algorithms successfully in automating ticket routing.

Potential for widespread industrial use across domains.

Abstract

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.