  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).
