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Semantic analysis for paraphrase identification using semantic role labeling

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Lee, E, Lynn, HM, Kim, HJ, Yeom, S ORCID: 0000-0002-5843-101X and Kim, P 2019 , 'Semantic analysis for paraphrase identification using semantic role labeling', paper presented at the 34th Annual ACM Symposium on Applied Computing, 8-12 April 2019, Limassol, Cyprus.

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Abstract

Reuse of documents has been prominently appeared during the course of digitalization of information contents owing to the wide-spread of internet and smartphones in various complex forms such as inserting words, omitting and substituting, changing word order, and etc. Especially, when a word in document is substituted with a similar word, it would be an issue not to consider it as a subject of measurement for the existing morphological similarity measurement method. In order to resolve this kind of problem, various researches have been conducted on the similarity measurement considering semantic information. This study is to propose a measurement method on semantic similarity being characterized as semantic role information in sentences acquired by semantic role labeling. To assess the performance of this proposed method, it was compared with the method of substring similarity being utilized for similarity measurement for existing documents. As a result, we could identify that the proposed method performed similar with the conventional method for the plagiarized documents which were rarely modified whereas it had improved results for paraphrasing sentences which were changed in structure.

Item Type: Conference or Workshop Item (Paper)
Authors/Creators:Lee, E and Lynn, HM and Kim, HJ and Yeom, S and Kim, P
Keywords: text reuse, text similarity, semantic role labeling
Journal or Publication Title: Proceedings of the 34th Annual ACM Symposium on Applied Computing
Publisher: Association for Computing Machinery
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