Automated Claim Identification Using NLP Features in Student Argumentative Essays
Abstract: The current study explores the ability to predict argumentative claims in structurally-annotated student essays to gain insights into the role of argumentation structure in the quality of persuasive writing. Our annotation scheme specified six types of argumentative components based on the well-established Toulmin’s model of argumentation. We developed feature sets consisting of word count, frequency data of key n-grams, positionality data, and other lexical, syntactic, semantic features based on both sentential and suprasentential levels. The suprasentential Random Forest model based on frequency and positionality features yielded the best results, reporting an accuracy of 0.87 and kappa of 0.73. This model will be included in an online writing assessment tool to generate feedback for student writers.