@inproceedings{a60c46d406d140ad82e62672f4ca53db,
title = "Word Graph Network: Understanding Obscure Sentences on Social Media for Violation Comment Detection",
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.",
keywords = "Social media, Violation comment detection, Word Graph Network",
author = "Dan Ma and Haidong Liu and Dawei Song",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020 ; Conference date: 14-10-2020 Through 18-10-2020",
year = "2020",
doi = "10.1007/978-3-030-60450-9_58",
language = "English",
isbn = "9783030604493",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "738--750",
editor = "Xiaodan Zhu and Min Zhang and Yu Hong and Ruifang He",
booktitle = "Natural Language Processing and Chinese Computing - 9th CCF International Conference, NLPCC 2020, Proceedings",
address = "Germany",
}
