Natural language processing (NLP) is rapidly developing for applications in
educational assessment. In this paper, I describe an NLP-based procedure that
can be used to support subject matter experts in establishing a crosswalk
between item specifications and content standards. This paper extends recent
work by proposing and demonstrating the use of multivariate similarity based on
embedding vectors for sentences or texts. In particular, a hybrid regression
procedure is demonstrated for establishing the match of each content standard
to multiple item specifications. The procedure is used to evaluate the match of
the Common Core State Standards (CCSS) for mathematics at grade 4 to the
corresponding item specifications for the 2026 National Assessment of
Educational Progress (NAEP).

利用自然语言处理（NLP）创建基于多变量相似度的混合回归程序，支持学科专家建立题目规范与内容标准的对应关系。在本文中，介绍了一个 NLP 的过程，通过嵌入向量对句子或文本进行相似度计算，并使用混合回归评估数学四年级的《共同核心教育标准》与 2026 年全国教育进展评估（NAEP）的题目规范的匹配程度。