融合Self-Attention机制和n-gram卷积核的 印尼语复合名词自动识别方法研究

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F_1值和32.34的短语单字识别F_1值,比SHOMA模型分别提升了4.93%和3.04%。

Automatic Recognition of Indonesian Compound Noun Phrases with a Combination of Self-Attention Mechanism and n-gram Convolution Kernel

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.