GazeGCN: Gaze-aware Graph Convolutional Network for Text Classification

Graph convolutional networks (GCNs) are capable of capturing contextual relationships in text classification. In this paper, we propose a novel Gaze-aware Graph Convolutional Network (GazeGCN) for text classification, where the gaze signals from humans are incorporated into the GCN architecture, empowering our GazeGCN to use gaze information that can be shared across all data to model the intricate relationships between words and documents. To be specific, we first build a gaze prediction model to obtain five gaze signals to form the gaze distribution of each word. Then, Wasserstein Distance is employed to derive the gaze-aware word-word weight by calculating the gaze distribution between words, so as to improve the capture of luxuriant and similar relationships between words. Furthermore, to enhance the extraction of contextual syntactic information, we introduce a Gaze-enhanced TF-IDF method integrating gaze signals and term frequency-inverse document frequency (TF-IDF) for gaze-aware word-document weight derivation, thus making up for the lack of TF-IDF that does not consider syntactic information. Afterward, the rich source of graph edge information including gaze-aware word-word weight and gaze-aware word-document weight is incorporated to construct graphs, so as to leverage the relationship of word-word and word-document. Experiments show encouraging results on seven benchmark datasets that our approach outperforms the state-of-the-art baseline methods.