Lexical Alignment Algorithm of Corpus Based on Multi-Feature Extraction

In the field of natural language processing, many tasks, including language learning and machine translation, need to find lexical level correspondences between texts in different languages. Finding this correspondence can improve the quality of machine translation and the accuracy of cross-language information retrieval. Therefore, this paper proposes to design a lexical alignment algorithm based on multi-feature extraction to solve this problem. In this paper, Taking the alignment accuracy rate, which can best reflect the performance of the alignment algorithm, as an example, the experimental group based on the proposed method has an average alignment accuracy of 96.97%, while the comparison group, that is, the alignment algorithm based on the multivariate knowledge base index based on Bert, has an average alignment accuracy of 94.53%. In other words, when compared with other resident languages, the proposed method still has advantages, so the multi-feature technique can be used to design the lexical alignment algorithm of the corpus well.