Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning

Mara Chinea-Rios, Marc Franco-Salvador, Yassine Benajiba


Abstract
The task of aspect extraction is an important component of aspect-based sentiment analysis. However, it usually requires an expensive human post-processing to ensure quality. In this work we introduce Aspect On, an interactive solution based on online learning that allows users to post-edit the aspect extraction with little effort. The Aspect On interface shows the aspects extracted by a neural model and, given a dataset, annotates its words with the corresponding aspects. Thanks to the online learning, Aspect On updates the model automatically and continuously improves the quality of the aspects displayed to the user. Experimental results show that Aspect On dramatically reduces the number of user clicks and effort required to post-edit the aspects extracted by the model.
Anthology ID:
2020.lrec-1.612
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4974–4981
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.612
DOI:
Bibkey:
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PDF:
https://aclanthology.org/2020.lrec-1.612.pdf