Syntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related entities than sentence representations. But, existing studies only use first-order syntactic relations (i.e., the arcs) in dependency trees to identify trigger words. For this reason, this paper proposes a new method for event detection, which uses a dependency tree based graph convolution network with aggregative attention to explicitly model and aggregate multi-order syntactic representations in sentences. Experimental comparison with state-of-the-art baselines shows the superiority of the proposed method.

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This paper proposes and designs a best path selection algorithm, which can solve the problem of path planning for intelligent driving vehicles in the case of restricted driving, traffic congestions and accidents. We tried to solve the problem under these emergency situations in path planing process for there's no driver in intelligent driving vehicle. We designed a new method of the best path selection with length priority based on the prior knowledge applied reinforcement learning strategy, and improved the search direction setting of A * shortest path algorithm in the program. This best path planing algorithm can effectively help different types of intelligent driving vehicles to select the best path in the traffic network with limited height, width and weight, accidents and traffic jams. Through simulation experiments and practical test, it is proved that the proposed algorithm has good stability, high efficiency and practicability. INDEX TERMS Reinforcement learning, intelligent driving, path planning, shortest path algorithm. I. INTRODUCTION

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