Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.

新冠肺炎的蔓延使得线上移动教学成为教育发展的必然趋势,本文以适合汉语教材自动推送的口语素材为研究对象,基于10341条生活类口语语料,对词汇的整体特点进行计量分析,在此基础上使用词向量模型及Kmeans算法对全部词语进行聚类,参考词语聚类结果及对口语语料话题和场景的考察,构建了一个包含15个一级话题、102个二级话题及81个交际场景的汉语口语话题-场景素材库。同时对各级话题常用词进行了总结。本文可为教材自动定制的素材库提供资源支持。