An automatic text comprehension classifier based on mental models and latent semantic features

Abstract

Reading comprehension is one of the main concerns for educational institutions, as it forges the students' ability to comprehend and learn accurately a given information source (e.g. textbooks, articles, papers, etc.). However, there are few approaches that integrates digital sources of educational information with automated systems to detect whether an individual has comprehended a given reading task. This work main contribution is a text comprehension classification methodology for the detection of reading comprehension failures in educational institutions. The proposed approach relates situational model theories and latent semantic analysis from fields of psycholinguistics and natural language processing respectively. A numerical characterization of students' documents using structural information, such as the usage of text connectors, and latent semantic features are used as input for traditional classification algorithms. Therefore, an automated classifier is built to determine whether a given student could or not comprehend the information in the given stimulus documents. For the evaluation of the proposed methodology, using a set of stimulus documents, a set of questions must be answered by an experimental group of students. We have performed experiments using first year students from Engineering and Linguistics undergraduate schools at the University of Chile with promising results.

Automatic Comprehension and Summarisation of Legal Contracts

“…Automatic text comprehension is a phenomenon that has been in existence since the early 70s and has since become popular in today's society [27,28]. Text comprehension is a far more complex task when compared to other computational tasks such as reading or writing (inputting).…”

“…Understanding a text automatically deduces that one has the ability to form an interconnected and cohesive interpretation of the information found in that particular text. There are several Natural language Processing (NLP) applications that require comprehension of source documents and it is important to note that the majority of those applications comprise of semantic processing of a natural language [28,29]. Automatic text summarisation is a process related to the construction of a brief and precise version of an original text with the use of a computational software program.…”

Tools for External Plagiarism Detection in DOCODE

“…Our approach is inspired by the work of [22], where the authors propose a plagiarism detection technique based on LSA. More details can be found in our related paper [23].…”