Unsupervised translation disambiguation based on web indirect association of bilingual word

To solve the problems of data sparseness and knowledge acquisition in translation disambiguation and WSD (word sense disambiguation), this paper introduces a fully unsupervised method, which is based on Web mining and Web indirect association of bilingual words. It provides new knowledge of translation disambiguation. It assumes that word sense can be determined by indirect association of bilingual words. Based on Web, this paper revises four common methods of indirect association, and designs three decision methods. These methods are evaluated on a gold standard Multilingual Chinese English Lexical Sample Task dataset of SemEval- 2007. The experimental results show that the model gets the state-of-the-art results (Pmar=44.4%) and outperforms the best system in SemEval-2007.