Abstract:

From the perspective of problem awareness, this paper conducted an in-depth terminology analysis on machine translation and manual translation by training vector space model. Three experiments were performed by the means of training the vector space model to compare the results of machine translation. These experiments demonstrate the similarity between machine translation and manual translation is 0.403. Integrated with computer technology, linguistics and translation, this paper focuses on the semantic similarity between machine translation and manual translation that aims to blaze a new way for results evaluation of machine translation.

Key words: machine translation, vector space model, petroleum terms, semantic similarity