Segmented Translation Algorithm of Complex Long Sentences Based on Semantic Features

Abstract

Abstract The progress of the times is inseparable from communication. If a country wants to develop well, it must learn from each other. Accurate language translation can better let people understand what they want to express. Therefore, language translation is becoming more and more important in the current social communication. Although there is a lot of research on translation, in many cases there will be inaccurate translations. Therefore, finding an accurate translation method is what many people need. Aiming at the problem that the translation of complex long sentences is prone to errors, this paper proposes a sentence segmentation algorithm, which is a method of dividing the long sentence into multiple independent clauses and then translating it. The segmentation algorithm uses the semantic features of the Concept Hierarchical Network (HNC) theory to segment clauses. The segmentation algorithm is integrated with a rule-based baseline translation system. The BLEU value of the integrated translation system reaches 0.1898, which is higher than that before the integration. The system has increased by 30%. Experimental results prove that the proposed method can effectively improve the effect of patent translation.