Title :
Determining Degree of Relevance of Reviews Using a Graph-Based Text Representation
Author :
Ramachandran, Lakshmi ; Gehringer, Edward F.
Abstract :
Reviews are text-based feedback provided by reviewers to authors. The quality of a review can be determined by identifying how relevant it is to the work that the review was written for as well as its similarity to existing well-written and coherent reviews. Relevance between two pieces of text can be determined by identifying semantic and syntactic similarities between them. In this paper, we make use of string-based metrics that incorporate concepts of paraphrasing and plagiarism to determine matching between texts. We use a graph-based text representation technique. We use the k-nearest neighbor classification algorithm to build a supervised model and classify text as LOW, MEDIUM or HIGH based on values of the metrics. We evaluate our approach on three data sets from student assignments and show that our model achieves an average accuracy of 63%.
Keywords :
graph theory; pattern classification; relevance feedback; string matching; text analysis; coherent reviews; graph-based text representation; k-nearest neighbor classification algorithm; paraphrasing; plagiarism; review quality; semantic similarity identification; string-based metrics; student assignments; supervised model; syntactic similarity identification; text classification; text matching; text-based feedback; Accuracy; Measurement; Plagiarism; Predictive models; Semantics; Syntactics; Vectors; graph-based representation; k-nearest neighbor; paraphrasing; plagiarism; relevance;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
DOI :
10.1109/ICTAI.2011.72