Problem
Abstract:
One of the elements that contribute to the nonuniformity of the question data in Helpdesk Ticketing Support (HTS) System is the diversity of services and users. Most questions that were asked in the HTS are in various forms and sentence styles but usually offer the same meaning. Various state-of-the-art machine-learning approaches have recently been used to automate the question classification process. Question classification, according to the researchers, is important to solve problems like helpdesk tickets being forwarded to the wrong resolver group and causing the ticket transfer process to take effect, and to associate a help desk ticket with its correct service from the start, reducing ticket resolution time, saving human resources, and improving user satisfaction. The key findings in the exploration results revealed that in HTS, tickets with a high number of transfer transactions take longer to complete than tickets with no transfer transaction. Thus, this research aims to develop an automated question classification model for the HTS and proposes to apply the supervised machine learning methods: Naïve Bayes (NB) and Support Vector Machine (SVM). The domain will use a readily available dataset from IT Unit. It is expected that this study will have a significant impact on the productivity of technical and system owners in dealing with the increasing number of comments, feedbacks, and complaints presented by end-users. This paper will present related works and research frameworks for automated question classification for HTS.
Task
I. For this abstract, what field/fields of study do the authors assume their audience is familiar with? How can you tell? List as many fields or subfields as you can. What are some fields/subfields that are not the target audience, but which may find this work useful?
II. Based on how the abstract is written, what is the argument of the essay/article? What sentence(s) explain this? To what extent is this a response to previous research?
III. What information seems to be the most condensed or combined in this abstract? In other words, where would you expect to see the most elaboration/expansion in the full text version?