Proposal and study of statistical features for string similarity computation and classification
For researchers in string similarity and plagiarism detection, the paper introduces a novel statistical approach that is language-agnostic and outperforms existing methods.
The paper proposes adaptations of co-occurrence matrix (COM) and run-length matrix (RLM) features for string similarity computation, showing they outperform existing statistical features in synthetic experiments (3 out of 4 cases with P-value < 0.001) and achieve best results on a real plagiarism dataset.
Adaptations of features commonly applied in the field of visual computing, co-occurrence matrix (COM) and run-length matrix (RLM), are proposed for the similarity computation of strings in general (words, phrases, codes and texts). The proposed features are not sensitive to language related information. These are purely statistical and can be used in any context with any language or grammatical structure. Other statistical measures that are commonly employed in the field such as longest common subsequence, maximal consecutive longest common subsequence, mutual information and edit distances are evaluated and compared. In the first synthetic set of experiments, the COM and RLM features outperform the remaining state-of-the-art statistical features. In 3 out of 4 cases, the RLM and COM features were statistically more significant than the second best group based on distances (P-value < 0.001). When it comes to a real text plagiarism dataset, the RLM features obtained the best results.