@inproceedings{tang-shen-2020-categorizing,
    title = "Categorizing Offensive Language in Social Networks: A {C}hinese Corpus, Systems and an Explainable Tool",
    author = "Tang, Xiangru  and
      Shen, Xianjun",
    editor = "Sun, Maosong  and
      Li, Sujian  and
      Zhang, Yue  and
      Liu, Yang",
    booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
    month = oct,
    year = "2020",
    address = "Haikou, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.ccl-1.97/",
    pages = "1045--1056",
    language = "eng",
    abstract = "Recently, more and more data have been generated in the online world, filled with offensive language such as threats, swear words or straightforward insults. It is disgraceful for a progressive society, and then the question arises on how language resources and technologies can cope with this challenge. However, previous work only analyzes the problem as a whole but fails to detect particular types of offensive content in a more fine-grained way, mainly because of the lack of annotated data. In this work, we present a densely annotated data-set COLA"
}