Computer Science ›› 2017, Vol. 44 ›› Issue (4): 252-255.doi: 10.11896/j.issn.1002-137X.2017.04.053

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Supervised WSD Method Based on Context Translation

YANG Zhi-zhuo   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In order to overcome the data sparseness problem for supervised WSD methods,this paper presented a WSD method based on context translation.The method assumes that the context consisted of the ambiguous words has the similar meaning as the context in the original.Under this assumption,first,a large number of pseudo training data are generated in the context of the target text.Then the Bayesian model is trained by utilizing both authentic and pseudo training data.Finally,the method performs word sense disambiguation by using Bayesian model.Experimental results show that the proposed method can significantly improve traditional WSD accuracy by 4.35%,and outperforms the best participating system in the SemEval-2007 evaluation.

Key words: Data sparseness,Context expansion,Machine translation,Pseudo training data,Bayesian model

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