利用语义词典Web挖掘语言模型的无指导译文消歧

为了解决困扰词义及译文消歧的数据稀疏及知识获取问题,提出一种基于Web利用n-gram统计语言模型进行消歧的方法.在提出词汇语义与其n-gram语言模型存在对应关系假设的基础上,首先利用Hownet建立中文歧义词的英文译文与知网DEF的对应关系并得到该DEF下的词汇集合,然后通过搜索引擎在Web上搜索,并以此计算不同DEF中词汇n-gram出现的概率,然后进行消歧决策.在国际语义评测SemEval-2007中的Multilingual Chinese English Lexical Sample Task测试集上的测试表明,该方法的P_mar值为55.9%,比其上该任务参评最好的无指导系统性能高出12.8%.

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

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 (P_mar=55.9%) and outperforms 12.8% on the best system in SemEval-2007.