基于关键词生成的网格事件相似度并行计算

为实现在海量网格事件库中快速、准确地检索事件, 本文提出一种基于关键词生成的网格事件相似度并行计算方法. 该方法通过双向LSTM网络的编码器和单向LSTM网络的解码器构建指针生成网络生成事件关键词, 使用记忆网络作为指针生成网络的序列信息存储单元, 并将注意力机制用在输入序列上以将更重要的信息输入至解码器, 同时引入覆盖机制来解决生成重复文本问题. 在生成事件关键词后, 基于结构相似度和情境相似度计算事件总体相似度, 并利用GPU对LSTM网络和相似度计算进行加速. 实验结果表明: 相比基于机器学习的计算方法, 该方法在事件相似度计算性能上更好, 最高获得了4.04倍的加速比.

Parallel Calculation of Grid Event Similarity Based on Keyword Generation

For quick and accurate retrieval in massive grid events, this study proposes the parallel similarity calculation of grid events based on keyword generation. The method generates grid event keywords through the pointer-generator network based on the encoder of a bidirectional LSTM network and the decoder of a unidirectional LSTM network. It uses a memory network to store sequence information and applies the attention mechanism to the input sequence, enabling more important information into the decoder. It also introduces the overwriting mechanism to avoid duplicate texts. After the keywords are generated, the overall similarity is calculated based on the structural similarity and situational similarity of events. GPU is utilized to accelerate the LSTM network and similarity calculation. Experimental results show that the method has better performance in similarity calculation than methods based on machine learning, with a speedup ratio reaching 4.04 times.