Unsupervised Translation Disambiguation by Using Semantic Dictionary and Mining Language Model from Web

Abstract

In order to solve the problem of data sparseness and knowledge acquisition in translation disambiguation and WSD(word sense disambiguation),this paper introduces an unsupervised method,based on the n-gram language model and web mining.It is supposed that there exists a latent relationship between the word sense and n-gram language model.Based on this assumption,the mapping between the English translation of Chinese word and the DEF of Hownet is established and the word set is acquired.Then the probabilities of n-gram in the words set are calculated based on the query results of a searching engine.The disambiguation is performed via these probabilities.This method is evaluated on a gold standard Multilingual Chinese English Lexical Sample Task dataset.Experimental results show that the model gets the state-of-the-art results(Pmar=55.9%) and outperforms 12.8% on the best system in SemEval-2007.