Message Passing Attention Networks for Document Understanding
By Giannis Nikolentzos, Antoine Jean-Pierre Tixier, Michalis Vazirgiannis. 2019
81 citations recorded by the source corpus.
Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many tasks. Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Experiments conducted on 10 standard text classification datasets show that our architectures are competitive with the state-of-the-art. Ablation studies reveal further insights about the impact of the different components on performance. Code is publicly available at: this https URL .
Subfields: Artificial Intelligence
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Citations in the corpus
- Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020, methodology): “Noticing a concurrent work (Nikolentzos et al., 2020) also reinforces the approach with a similar graph network structure, we describe the similarities and differences in the method section.”
- Text Graph Transformer for Document Classification (2020, background): “Recently, graph based text classification models received growing attention due to its ability to model global information in corpus (Yao et al., 2019; Peng et al., 2018; Zhang et al., 2020b; Nikolentzos et al., 2019).”