Exploring the Use of Lexicons to aid Deep Learning towards the Detection of Abusive Language

Anna Koufakou, Jason Scott


Abstract
Detecting abusive language is a significant research topic, which has received a lot of attention recently. Our work focused on detecting personal attacks in online conversations. State-of-the-art research on this task has largely used deep learning with word embeddings. We explored the use of sentiment lexicons as well as semantic lexicons towards improving the accuracy of the baseline Convolutional Neural Network (CNN) using regular word embeddings. This is a work in progress, limited by time constraints and appropriate infrastructure. Our preliminary results showed promise for utilizing lexicons, especially semantic lexicons, for the task of detecting abusive language.
Anthology ID:
W19-3640
Volume:
Proceedings of the 2019 Workshop on Widening NLP
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS | WiNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
129–131
Language:
URL:
https://www.aclweb.org/anthology/W19-3640
DOI:
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