Methods for analysis and evaluation of the semantic content of a writing based on vector length

Abstract

The present invention is a methodology for analyzing and evaluating a sample text, such as essay(s), or document(s). This methodology compares sample text to a reference essay(s), document(s), or text segment(s) within a reference essay or document. The methodology analyzes the amount of subject-matter information in the sample text, analyzes the relevance of subject matter information in the sample and evaluates the semantic coherence of the sample. This methodology presumes there is an underlying, latent semantic structure in the usage of words. The method parses and stores text objects and text segments from the sample text and reference text into a two-dimensional data matrix. A weight is computed for each text object and applied to each data matrix cell value. The method performs a singular value decomposition on the data matrix, which produces three trained matrices. The method computes a vector representation of the sample text and reference text using the three trained matrices. The methodology compares the sample text to the reference text by computing the cosine between the vector representation of the sample text and the vector representation of the standard reference text. Alternatively, the dot product is used to compare the sample text to the standard reference text. A grade is assigned to the sample text based on the degree of similarity between the sample text and the standard reference text.