2020/11/14 by Nabarun Mondal, Mondal, Nabarun, Mrunal Lohia +1
Computer Science · #FOS: Computer and information sciences #H.3.1 #I.7.m #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Software Engineering (cs.SE) #cs.IR #cs.LG #cs.SE
paper · pdf · doi:10.48550/arxiv.2011.13832
arxiv created 2020/11/30 · arxiv updated 2020/12/02
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.