Finally, we determine the final toxic spans using a prevalence-based fusion of the predictions generated by these models. As the toxic span detection task also requires us to apprehend the phrasal context for detecting toxic span, the similarity between these two tasks inspires us to exploit these NER models. The NER-based models usually intend to capture the contextual attributes of phrases and spans that are essential for named entity recognition. We explore an ensemble of sequence labeling models including the word embedding-based Spark NLP NER (named entity recognition) deep learning model, spaCy NER model with custom toxic tags, and ALBERT NER model to identify the toxic spans.

This paper describes our proposed approach to detect the toxic portions from text contents efficiently and accurately. Detecting the toxic portions is essential as it substantially aids to moderate the toxic contents through excluding the abusive parts. Numerous state-of-the-art approaches classify the whole content as toxic or non-toxic, but they donвЂ™t distinguish the precise toxic portion from the whole content. Hence, identifying these toxic contents and taking appropriate measures against them is crucial to preserve a safe environment on social media. It often breaks peopleвЂ™s confidence and causes extensive damage to their mental health. Negative and hateful comments are averting users from sharing their opinion freely on social media platforms. The upsurge of prolific blogging and microblogging platforms enabled the abusers to spread negativity and threats greater than ever.

Toxic tagger