Intent classification for a management conversational assistant
Hefny, Abdelrahman, Dafoulas, George 
ORCID: https://orcid.org/0000-0003-2638-8771 and Ismail, Manal
(2020)
Intent classification for a management conversational assistant.
15th International Conference on Computer Engineering and Systems Proceedings.
In: ICCES 2020, 15-16 Dec 2020, Cairo, Egypt.
e-ISBN 9780738105598, pbk-ISBN 9780738105604.
[Conference or Workshop Item]
(doi:10.1109/ICCES51560.2020.9334685)
|
PDF
- Final accepted version (with author's formatting)
Download (234kB) | Preview |
Abstract
Intent classification is an essential step in processing user input to a conversational assistant. This work investigates techniques of intent classification of chat messages used for communication among software development teams with the aim of building an intent classifier for a management conversational assistant integrated into modern communication platforms used by developers. Experiments conducted using rule-based and common ML techniques have shown that careful choice of classification features has a significant impact on performance, and the best performing model was able to obtain a classification accuracy of 72%. A set of techniques for extracting useful features for text classification in the software engineering domain was also implemented and tested.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Research Areas: | A. > School of Science and Technology > Computer Science > Intelligent Environments group |
| Item ID: | 32282 |
| Notes on copyright: | Copyright © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Useful Links: | |
| Depositing User: | George Dafoulas |
| Date Deposited: | 09 Apr 2021 09:36 |
| Last Modified: | 20 Feb 2023 17:49 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/32282 |
Actions (login required)
 |
View Item |
Statistics
Additional statistics are available via IRStats2.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)