Using the BP neural networks to Chinese word sense disambiguation
Fang Xie · Journal of Central China Normal University · 2005
Word sense disambiguation(WSD) is a difficult issue in many fields of natural language processing,e.g.machine translation,information retrieval and hyertext navigation,context and thematic analysis,etc.This paper firstly introduces a Chinese WSD model which is combining the BP neural networks and statistics method,and then discusses the feasibility and advantage of this WSD model.At the last,it finds that the error between the actual and predictive gather is fluctuant through the experiment,namely,the experiment error does not have the notable trend to zero by increasing iterative times,and it can get the better results from the less iterative times.Accordingly,the BP neural network model has the good application foreground in WSD.