If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Text classification is a primary task in natural language processing (NLP). Recently, graph neural networks (GNNs) have developed rapidly and been applied to text classification tasks. Although more complex models tend to achieve better performance, research highly depends on the computing power of the device used. In this article, we propose TENT (https://github.com/Daisean/TENT) to obtain better text classification performance and reduce the reliance on computing power. Specifically, we first establish a dependency analysis graph for each text and then convert each graph into its corresponding encoding tree. The representation of the entire graph is obtained by updating the representation of the non-leaf nodes in the encoding tree. Experimental results show that our method outperforms other baselines on several datasets while having a simple structure and few parameters.
In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the complexity of such models. Here, based on an optimization method, we investigate the feasibility of improving graph classification performance while simplifying the model learning process. Inspired by progress in structural information assessment, we optimize the given data sample from graphs to encoding trees. In particular, we minimize the structural entropy of the transformed encoding tree to decode the key structure underlying a graph. This transformation is denoted as structural optimization. Furthermore, we propose a novel feature combination scheme, termed hierarchical reporting, for encoding trees. In this scheme, features are transferred from leaf nodes to root nodes by following the hierarchical structures of encoding trees. We then present an implementation of the scheme in a tree kernel and a convolutional network to perform graph classification. The tree kernel follows label propagation in the Weisfeiler-Lehman (WL) subtree kernel, but it has a lower runtime complexity $O(n)$. The convolutional network is a special implementation of our tree kernel in the deep learning field and is called Encoding Tree Learning (ETL). We empirically validate our tree kernel and convolutional network with several graph classification benchmarks and demonstrate that our methods achieve better performance and lower computational consumption than competing approaches.