一种改进的无监督网络图词义消歧方法研究

词义消歧是一项根据上下文自动选择正确词义的任务,并且成为了计算语言学领域中最重要最有挑战性的难题之一,在各种自然语言处理应用程序中起了至关重要的作用.因此,为了提高词义消歧的准确率,提出一种改进的无监督网络图词义消歧方法.使用《知网》HowNet作为知识库,运用一种新的词语间高阶关系的相似性度量方法,来给图的边分配适当的权值.然后,使用中心度计算并且结合相邻词义,来选择最适合目标词的方法.在数据集Senseval-3中进行了具体测试.实验结果显示:提出的方法的准确率达到46.1%,优于相同测试集下其他无监督词义消歧方法.

An improved unsupervised word sense disambiguation method based on network graph

Word sense disambiguation is an automatic selection of correct meaning according to the context of the task,and has become one of the most challenging and the most important problems in the field of computational linguistics,and plays a crucial role in a variety of applications in natural language processing. Therefore,in order to improve the accuracy of word sense disambiguation,an improved unsupervised word sense disambiguation meth-od was proposed. Using "HowNet"as a knowledge base,the method used similarity of a high order new relation-ship between words measurement method to assign appropriate weights to the edges of a graph. Then,the method was used to select the best fit for the target word. The experiments were carried out in the data set Senseval-3, and the experimental results show that the proposed method could achieve an accuracy of 46. 1%,which is better than other unsupervised word sense disambiguation methods under the same test set.