Problem Classification for Tailored Helpdesk Auto-replies

Lecture Notes in Computer Science, 2018

Ticket assignment/dispatch is a crucial part of service delivery business with lot of scope for automation and optimization. In this paper, we present an end-to-end automated helpdesk email ticket assignment system, which is also offered as a service. The objective of the system is to determine the nature of the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group (or team) for resolution. The proposed system uses an ensemble classifier augmented with a configurable rule engine. While design of classifier that is accurate is one of the main challenges, we also need to address the need of designing a system that is robust and adaptive to changing business needs. We discuss some of the main design challenges associated with email ticket assignment automation and how we solve them. The design decisions for our system are driven by high accuracy, coverage, business continuity, scalability and optimal usage of computational resources. Our system has been deployed in production of three major service providers and currently assigning over 40,000 emails per month, on an average, with an accuracy close to 90% and covering at least 90% of email tickets. This translates to achieving human-level accuracy and results in a net saving of about 23000 man-hours of effort per annum.

Lecture Notes in Computer Science, 2006

We present a comparative study of corpus-based methods for the automatic synthesis of email responses to help-desk requests. Our methods were developed by considering two operational dimensions: (1) information-gathering technique, and (2) granularity of the information. In particular, we investigate two techniques-retrieval and prediction-applied to information represented at two levels of granularity-sentence level and document level. We also developed a hybrid method that combines prediction with retrieval. Our results show that the different approaches are applicable in different situations, addressing a combined 72% of the requests with either complete or partial responses.

Web Intelligence, 2003. WI 2003. …, 2003

In this paper we present a neural network based system for automated e-mail filing into folders and antispam filtering. The experiments show that it is more accurate than several other techniques. We also investigate the effects of various feature selection, weighting and normalization methods, and also the portability of the anti-spam filter across different users.

2009

Email occupies a central role in the modern workplace. This has led to a vast increase in the number of email messages that users are expected to handle daily. Furthermore, email is no longer simply a tool for asynchronous online communication—email is now used for task management, personal archiving, as well both synchronous and asynchronous online communication (Whittaker and Sidner 1996). This explosion can lead to “email overload”—many users are overwhelmed by the large quantity of information in their mailboxes.

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

Technical support problems are often long and complex. They typically contain user descriptions of the problem, the setup, and steps for attempted resolution. Often they also contain various non-natural language text elements like outputs of commands, snippets of code, error messages or stack traces. These elements contain potentially crucial information for problem resolution. However, they cannot be correctly parsed by tools designed for natural language. In this paper, we address the problem of segmentation for technical support questions. We formulate the problem as a sequence labelling task, and study the performance of state of the art approaches. We compare this against an intuitive contextual sentence-level classification baseline, and a state of the art supervised text-segmentation approach. We also introduce a novel component of combining contextual embeddings from multiple language models pre-trained on different data sources, which achieves a marked improvement over using embeddings from a single pre-trained language model. Finally, we also demonstrate the usefulness of such segmentation with improvements on the downstream task of answer retrieval.

IEEE Computational Intelligence Bulletin, 2003

This paper discusses the design and the approach we have developed in order to deal effectively with customer emails sent to a corporation. We first present the current state of the art and then make the point that natural language tools are needed in order to deal effectively with the rather informal style encountered in the e-mails. In our project, called Mercure, we have explored three complementary approaches: classification, case-based reasoning and question-answering.

Proceedings of the Annual Hawaii International Conference on System Sciences, 2022

Authorea (Authorea), 2023

Service desk systems have a vast and rich base of information, consisting of the history of calls made, which can and should be used as a reference base for subsequent calls. Common search tools, such as keyword searches, prove to be unfeasible in large datasets, in addition to being able to bring results not necessarily related to the problem. "State-of-the-art" techniques exist, but they require high computational and operational costs for their training and use. In this sense, the purpose of this work is to investigate the sensitivity of machine learning algorithms in finding the characteristic defined here as "relevance": the characteristic of texts with knowledge that can be reused. The motivation is that non-relevant texts can be removed in advance from the database, allowing complex algorithms to be employed in a more condensed database, reducing computational costs. Tests were performed with several combinations between the TF-IDF vectorizer and the word embedding Doc2Vec and the classic classifiers Naive-Bayes, Adaptive Boosting, Random Forest, Stochastic Gradient Descent, Logistic Regression, Support Vector Machine and Light Gradient Boosting Machine, and the classifier TextConvoNet, an architecture based on Convolutional Neural Networks. The TextConvoNet classifier presented the best results, with metrics close to 0.93, showing that the concept is detectable and that the technique is viable for removing non-relevant texts from a database.

2021

Processing natural language and extract relevant information in deep technical engineering domain remains an open challenge. On the other side, manufacturers of high-value assets which often deliver product services through the equipment life, supporting maintenance, spare parts management and remote monitoring and diagnostics for issues resolution, have availability of a good amount of textual data containing technical cases with a certain engineering depth. This paper presents a case study in which various Artificial Intelligence algorithms were applied to historical technical cases to extract know-how useful to help technicians in approaching new cases. Initially the work process and available data are presented; the focus is on the outbound communication delivered from the technical team to the site operators, that is structured in 3 main paragraphs: event description, technical assessment, recommended actions. The work proceeded in two parallel streams: the first concerned the ...

2023

The technical support department or helpdesk department is a unit that requires a quick response in handling its tasks. The company's helpdesk team can consist of several individuals who know specific or specialized issues. Typically, technical problems are handled with an application that can track issues based on tickets. Ticket queue systems are used to facilitate control over the actions of the service or repair provided by the team. Helpdesk applications assist in addressing issues reported by users and then help upper-level management distribute tasks and monitor the helpdesk team's performance, including providing solutions to users' various problems. This research aims to predict the placement of fields that serve assistance based on the corpus users provide in the natural language. Prediction modelling is done using the Naïve Bayes and Support Vector Machine algorithms. The modelling results show that the accuracy rate of helpdesk service prediction with the Naïve Bayes algorithm reaches 82.06%, while the accuracy rate of prediction with the Support Vector Machine algorithm reaches 85.30%.