A Graph Convolutional Network‐Based Sensitive Information Detection Algorithm

Abstract

In the field of natural language processing (NLP), the task of sensitive information detection refers to the procedure of identifying sensitive words for given documents. The majority of existing detection methods are based on the sensitive-word tree, which is usually constructed via the common prefixes of different sensitive words from the given corpus. Yet, these traditional methods suffer from a couple of drawbacks, such as poor generalization and low efficiency. For improvement purposes, this paper proposes a novel self-attention-based detection algorithm using the implementation of graph convolutional network (GCN). The main contribution is twofold. Firstly, we consider a weighted GCN to better encode word pairs from the given documents and corpus. Secondly, a simple, yet effective, attention mechanism is introduced to further integrate the interaction among candidate words and corpus. Experimental results from the benchmarking dataset of THUC news demonstrate a promising detection performance, compared to existing work.

Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method

“…Wu et al [12] modeled documents as graphs to construct DAGNNs, using graphs to model each document so that it can capture discontinuous semantics and long-distance semantics. In recent years, a large number of studies have exploited this feature to explore the application of graph convolutional neural networks to text classification tasks to express semantic relationships in a text [13][14][15][16][17][18][19]. The vector initialization of these model graph nodes is often based on the degree of the node, and the edge information is encoded one-hot, missing the semantic information of the word or words themselves.…”