Multigraph Convolution Network with Jump Connection for Event Detection Event detection is an important information extraction task in nature language processing. Recently, the method based on syntactic information and graph convolution network has been wildly used in event detection task and achieved good performance. For event detection, graph convolution network (GCN) based on dependency arcs can capture the sentence syntactic representations and the syntactic information, which is from candidate triggers to arguments. However, existing methods based on GCN with dependency arcs suffer from imbalance and redundant information in graph. To capture important and refined information in graph, we propose Multigraph Convolution Network with Jump Connection (MGJED). The multigraph convolution network module adds a core subgraph splitted from dependency graph which selects important onehop neighborsu0027 syntactic information in breadth via GCN. Also the jump connection architecture aggregate GCN layersu0027 representation with different attention score, which learns the importance of neighborsu0027 syntactic information of different hops away in depth. The experimental results on the widely used ACE 2005 dataset shows the superiority of the other stateoftheart methods.

Improving Action Recognition with the GraphNeuralNetworkbased Interaction Reasoning Recent human action recognition methods mainly model a twostream or 3D convolution deep learning network, with which humans spatialtemporal features can be exploited and utilized effectively. However, due to the ignoring of interaction exploiting, most of these methods cannot get good enough performance. In this paper, we propose a novel action recognition framework with Graph Convolutional Network (GCN) based Interaction Reasoning: Objects and discriminative scene patches are detected using an object detector and class active mapping (CAM), respectively; and then a GCN is introduced to model the interaction among the detected objects and scene patches. Evaluation of two widely used video action benchmarks shows that the proposed work can achieve comparable performance: the accuracy up to 43.6% at EPIC Kitchen, and 47.0% at VLOG benchmark without using optical flow, respectively.