作　　者： ;

机构地区： 广东外语外贸大学

出　　处： 《湖南工业大学学报》 2020年第3期1-9,共9页

摘　　要： 针对印尼语复合名词短语自动识别,提出一种融合Self-Attention机制、n-gram卷积核的神经网络和统计模型相结合的方法,改进现有的多词表达抽取模型。在现有SHOMA模型的基础上,使用多层CNN和Self-Attention机制进行改进。对Universal Dependencies公开的印尼语数据进行复合名词短语自动识别的对比实验,结果表明:TextCNN+Self-Attention+CRF模型取得32.20的短语多词识别F1值和32.34的短语单字识别F1值,比SHOMA模型分别提升了4.93%和3.04%。 In view of the automatic recognition of Indonesian compound noun phrases,this paper proposes a method with Self-Attention mechanism,n-gram convolution kernel neural network and statistical model combined together so as to improve the performance of the existing multi-word expression extraction model.On the basis of the existing SHOMA model,a further improvement can be made by using the multi-layer CNN and Self-Attention mechanism,followed by an automatic recognition of compound noun phrases based on Indonesian data disclosed by Universal Dependencies.The comparative experiment results show that the F1 multi-word phrase recognition value of 32.20,as well as the F1 single-word recognition value of 32.34 obtained by TextCNN+Self-Attention+CRF model obtains respectively is 4.93% and 3.04% respectively higher than that of SHOMA model.

关 键 词： 印尼语复合名词短语 机制 卷积神经网络 自动识别 条件随机场

领　　域： [自动化与计算机技术—计算机软件与理论] [自动化与计算机技术—计算机科学与技术]