Alokaili, A., Aletras, N. and Stevenson, M. orcid.org/0000-0002-9483-6006 (2019) Re-ranking words to improve interpretability of automatically generated topics. In: Dobnik, S., Chatzikyriakidis, S. and Demberg, V., (eds.) Proceedings of 13th International Conference on Computational Semantics (IWCS). 13th International Conference on Computational Semantics (IWCS), 23-27 May 2019, Gothenburg, Sweden. Association for Computational Linguistics (ACL) , pp. 43-54. ISBN 9781950737192
Abstract
Topics models, such as LDA, are widely used in Natural Language Processing. Making their output interpretable is an important area of research with applications to areas such as the enhancement of exploratory search interfaces and the development of interpretable machine learning models. Conventionally, topics are represented by their n most probable words, however, these representations are often difficult for humans to interpret. This paper explores the re-ranking of topic words to generate more interpretable topic representations. A range of approaches are compared and evaluated in two experiments. The first uses crowdworkers to associate topics represented by different word rankings with related documents. The second experiment is an automatic approach based on a document retrieval task applied on multiple domains. Results in both experiments demonstrate that re-ranking words improves topic interpretability and that the most effective re-ranking schemes were those which combine information about the importance of words both within topics and their relative frequency in the entire corpus. In addition, close correlation between the results of the two evaluation approaches suggests that the automatic method proposed here could be used to evaluate re-ranking methods without the need for human judgements.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2019 ACL. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Apr 2019 11:18 |
Last Modified: | 25 Aug 2020 10:20 |
Published Version: | https://www.aclweb.org/anthology/W19-0404/ |
Status: | Published |
Publisher: | Association for Computational Linguistics (ACL) |
Refereed: | Yes |
Identification Number: | 10.18653/v1/W19-0404 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144795 |