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2021 | OriginalPaper | Chapter

BERT-Capsule Model for Cyberbullying Detection in Code-Mixed Indian Languages

Authors : Krishanu Maity, Sriparna Saha

Published in: Natural Language Processing and Information Systems

Publisher: Springer International Publishing

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Abstract

In this work, we have created a benchmark corpus for cyberbullying detection against children and women in Hindi-English code-mixed language. Both these languages are the medium of communication for a large majority of India, and mixing of languages is widespread in day-to-day communication. We have developed a model based on BERT, CNN along with GRU and capsule networks. Different conventional machine learning models (SVM, LR, NB, RF) and deep neural network based models (CNN, LSTM) are also evaluated on the developed dataset as baselines. Our model (BERT+CNN+GRU+Capsule) outperforms the baselines with overall accuracy, precision, recall and F1-measure values of 79.28%, 78.67%, 81.99% and 80.30%, respectively.

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previous chapter Sequence-Based Word Embeddings for Effective Text Classification

next chapter Multiword Expression Features for Automatic Hate Speech Detection

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Title: BERT-Capsule Model for Cyberbullying Detection in Code-Mixed Indian Languages
Authors: Krishanu Maity
Sriparna Saha
Publisher: Springer International Publishing
Book: Natural Language Processing and Information Systems
Print ISBN: 978-3-030-80598-2

Electronic ISBN: 978-3-030-80599-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-80599-9_13

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