# Problem Classification for Tailored Helpdesk Auto-Replies

arXiv: https://arxiv.org/abs/2211.07603 · HTML: https://mlsanity.com/paper/2211.07603/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 11 Sep 2022.

## Builds on (most cited first, 1 of 1)

- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Machine Learning Approach to Classifying Construction Cost Documents into the International Construction Measurement Standard](https://mlsanity.com/paper/2211.07705/) (2022)

## Abstract (arXiv)

IT helpdesks are charged with the task of responding quickly to user queries. To give the user confidence that their query matters, the helpdesk will auto-reply to the user with confirmation that their query has been received and logged. This auto-reply may include generic `boiler-plate' text that addresses common problems of the day, with relevant information and links. The approach explored here is to tailor the content of the auto-reply to the user's problem, so as to increase the relevance of the information included. Problem classification is achieved by training a neural network on a suitable corpus of IT helpdesk email data. While this is no substitute for follow-up by helpdesk agents, the aim is that this system will provide a practical stop-gap.