engleski

Relevance of Similarity Measures Usage for Paraphrase Detection

The article describes the experiments and their results using two Deep Learning (DL) models and four measures of similarity/distance, determining the similarity of documents from the three publicly available corpuses of paraphrased documents. As DL models, Word2Vec was used in two variants and FastText in one. The article explains the existence of a multitude of hyperparamethers and defines their values, selection of effective way of text processing, the use of some non- standard parameters in Natural Language Processing (NLP), the characteristics of the corpuses used, the results of the pairs (DL model, similarity measure) processing corpuses, and seeks to determine combinations of conditions under which use of exactly certain pairs yields the best results (presented in the article), measured by standard evaluation measures Accuracy, Precision, Recall and primarily F-measure.

Plagiarism ; Deep Learning ; Word2Vec ; FastText ; Natural Language Processing ; Text Similarity ; Distance Measures ; Similarity Measures ; Euclidean ; Manhattan ; Cosine ; Soft Cosine ; Vector Space

Indeksirano i u: Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Microsoft Academic, Engineering Index (EI).