采用机器学习方法的企业IT支持帮助台流程混合自动化解决方案
Corporate IT-support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach
摘要：
大型组织中的综合IT支持团队需要更多的人力来处理来自不同渠道的员工的参与和请求。自动化电子邮件技术查询帮助台建议有即时实时快速解决方案和电子邮件分类。电子邮件主题建模与各种机器学习，深度学习方法进行了比较，具有不同的功能，可扩展的，通用的解决方案以及可靠的静态规则。电子邮件的标题、正文、附件、OCR文本和一些功能设计的自定义功能作为输入元素。XGBoost级联分层模型，具有词嵌入的Bi-LSTM模型表现良好，对于真实世界的企业电子邮件数据集，整体准确率为77.3。通过引入阈值技术，整个自动化系统架构为现实世界的企业电子邮件提供了85.6%的准确率。快速修复、静态规则和ML分类相结合，作为一种低成本的推理解决方案，在自动化和实时的实施过程中减少了81%的人力。
英文摘要：
Comprehensive IT support teams in large scale organizations require more man power for handling engagement and requests of employees from different channels on a 24*7 basis. Automated email technical queries help desk is proposed to have instant real-time quick solutions and email categorisation. Email topic modelling with various machine learning, deep-learning approaches are compared with different features for a scalable, generalised solution along with sure-shot static rules. Email's title, body, attachment, OCR text, and some feature engineered custom features are given as input elements. XGBoost cascaded hierarchical models, Bi-LSTM model with word embeddings perform well showing 77.3 overall accuracy For the real world corporate email data set. By introducing the thresholding techniques, the overall automation system architecture provides 85.6 percentage of accuracy for real world corporate emails. Combination of quick fixes, static rules, ML categorization as a low cost inference solution reduces 81 percentage of the human effort in the process of automation and real time implementation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
