Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2021

Abstract

Identifying pornographic text online is practically useful to protect children from access to such adult content. However, some authors may intentionally avoid using sensitive words in their pornographic texts to take advantage of the lack of human audits. Without prior knowledge guidance, real semantics of such pornographic text is difficult to understand by existing methods due to its high context-sensitivity and heavy usage of figurative language, which brings huge challenges to the porn detection systems used in social media platforms. In this paper, we approach to the problem as a document-level porn identification task by locating and integrating sentence-level evidence and propose a novel Evidence-Aware Neural Porn Classification (eNPC) model. Specifically, we first propose a basic model which locates porn indicative sentences in the document with a multiple instance learning model, and then aggregate the sentence-level evidence to induce document label with self-attention mechanism. Moreover, we consider label dependencies within local context. Finally, we further enhance the sentence representation with prior knowledge produced by an automatic porn lexicon construction strategy. Extensive experimental results show that our model exhibits consistent superiority over competitors on two real-world Chinese novel datasets and an English story dataset.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021): February 2-9, Virtual

First Page

14939

Last Page

14947

Publisher

AAAI Press

City or Country

Palo Alto, CA

Additional URL

https://ojs.aaai.org/index.php/AAAI/article/view/17753

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