Distant Supervision Relation Extraction via Multi-level Weight Optimization
To deal with the unproper sentence bag weight distribution and insufficient extraction of key features of sentences in current relation extraction models,this paper proposes a multi-level weight optimization method for distant supervision relation extraction.At the sentence bag level,an encoding and decoding network is applied to ob-tain the representation vector of the sentence and then the sentence bags are reconstructed,which makes the division of sentence bags more balanced.At the sentence level,the shortest dependency path attention mechanism is adopted to increase the weight of keywords and the ability to extract key features.Experiments on the public data set NYT show the method reaches 79％accuracy in average,which is about 3％improvement compared with the competitive mainstream methods.
万方数据
维普
