Named entity translation method based on machine translation lexicon

Abstract

In the context of the rapid development of computer technology, communication between various languages has become increasingly important. Among the research methods of named entities, the research on named entity translation methods based on machine translation is one of the research hotspots. Named entity translation is to realize the switching between entities in two languages, which can be used by browsers, translators, etc., and can greatly reduce the cost of communication between people from all over the world. Due to the immaturity of the existing translation model technology and the lack of integration, the translation of bilingual named entities with unique composition is very challenging. Based on this, this paper proposes a fusion method of bilingual entity class named entity translation based on chunk symmetry strategy and English–Chinese transliteration model based on machine learning strategy. According to the bilingual corpus, a more standard candidate entity translation pair is generated through bilingual named entity alignment. The transliteration model is used to reorder and correct the candidate translation results, so as to achieve the correct selection of translation pairs. Experiments show that the model based on the translation of named entity translations based on chunks and transliteration models based on machine learning strategies not only effectively solves the problem of difficulty in word ordering and selection in bilingual translation, but also makes extraction to a certain extent. The accuracy of the translation results has been improved.