Indexing Student Essays Paragraphs Using LSA Over An Integrated Ontological Space
نویسندگان
چکیده
A full understanding of text is out of reach of current human language technology. However, a shallow Natural Language Processing (NLP) approach can be used to provide automated help in the evaluation of essays. The main idea of this paper is that Latent Semantic Indexing (LSA) can be used in conjunction with ontologies and First order Logic (FOL) to locate segments relevant to a question in a student essay. Our test bed, in a first instance, is a set of ontologies such the AKT reference ontology (describing academic life), Newspaper and a Koala ontology (concerning koalas’ habitat).
منابع مشابه
Automated Assessment of Paragraph Quality: Introduction, Body, and Conclusion Paragraphs
Natural language processing and statistical methods were used to identify linguistic features associated with the quality of student-generated paragraphs. Linguistic features were assessed using Coh-Metrix. The resulting computational models demonstrated small to medium effect sizes for predicting paragraph quality: introduction quality r2 = .25, body quality r2 = .10, and conclusion quality r2...
متن کاملAn Overview of LSA-Based Systems for Supporting Learning and Teaching
Latent Semantic Analysis (LSA, [1]) is a well-known technique that captures semantic information in texts by uncovering word-usage regularities. Extensive research on LSA has proven its efficiency in the domain of natural language processing, and more specifically for computer-based instruction—tutoring systems, interactive learning environments [2]. The power of LSA lies in its versatility, re...
متن کاملOn the Effectiveness of Using Syntactic and Shallow Semantic Tree Kernels for Automatic Assessment of Essays
This paper is concerned with the problem of automatic essay grading, where the task is to grade student written essays given course materials and a set of humangraded essays as training data. Latent Semantic Analysis (LSA) has been used extensively over the years to accomplish this task. However, the major limitation of LSA is that it only retains the frequency of words by disregarding the word...
متن کاملLatent Semantic Analysis 1 Running head: LATENT SEMANTIC ANALYSIS AND KNOWLEDGE ASSESMENT Using Latent Semantic Analysis to assess knowledge: Some technical considerations
In a previous paper (Wolfe, Schreiner, Rehder, Laham, Foltz, Landauer, & Kintsch, this issue) we have shown how Latent Semantic Analysis (LSA) can be used to assess student knowledge how essays can be graded by LSA and how LSA can match students with appropriate instructional texts. We did this by comparing an essay written by a student with one or more target instructional texts in terms of th...
متن کاملPredicting Interest: Another Use for Latent Semantic Analysis
Latent Semantic Analysis (LSA) is a statistical technique for extracting semantic information from text corpora. LSA has been used with success to automatically grade student essays (Intelligent Essay Scoring), model human language learning, and model language comprehension. We examine how LSA may help to predict a reader’s interest in a selection of news articles, based on their reported inter...
متن کامل