Year: 2014
Type: conference-paper
Abstract: The ability to analyze the adequacy of supporting information is necessary for determining the strength of an argument.1 This is especially the case for online user comments, which often consist of arguments lacking proper substantiation and reasoning.Thus, we develop a framework for automatically classifying each proposition as UNVERIFIABLE, VERIFIABLE NON-EXPERIENTIAL, or VERIFIABLE EXPE-RIENTIAL 2 , where the appropriate type of support is reason, evidence, and optional evidence, respectively 3 .Once the existing support for propositions are identified, this classification can provide an estimate of how adequately the arguments have been supported.We build a goldstandard dataset of 9,476 sentences and clauses from 1,047 comments submitted to an eRulemaking platform and find that Support Vector Machine (SVM) classifiers trained with n-grams and additional features capturing the verifiability and experientiality exhibit statistically significant improvement over the unigram baseline, achieving a macro-averaged F 1 of 68.99%.
DOI: 10.3115/v1/w14-2105
Authors: Joonsuk Park, Claire Cardie
Institution: Cornell University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W2250287365