Abstract
Violation comment detection aims to recognize the texts that may violate the governing laws/regulations and cause adverse effect on social media. To avoid being intercepted, violation comments always informal and incomplete in an obscure expression poses challenge to violation detection algorithms. To tackle the problem, we introduce a new language representation model namely Word Graph Network (WGN). By introducing word graph, WGN integrates more syntactic structure information thus is qualified with stronger association and completion capability on detecting informal and incomplete violation comments in social networking scenarios. Our experimental results show that WGN outperforms than the existing state-of-the-art models and even performs best in simulation of real online environment.
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Notes
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https://hello.yy.com. It should be noted that the collected data doesn’t contain the user information or other sensitive information.
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The datasets can be downloaded from https://github.com/Cczt121/WGN-datasets.
- 4.
It can be downloaded from https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip.
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Ma, D., Liu, H., Song, D. (2020). Word Graph Network: Understanding Obscure Sentences on Social Media for Violation Comment Detection. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_58
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