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132078 - Semantic analysis for paraphrase identification using semantic role labeling.pdf (739.03 kB)
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Semantic analysis for paraphrase identification using semantic role labeling

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conference contribution
posted on 2023-05-23, 14:04 authored by Lee, E, Lynn, HM, Kim, HJ, Soonja YeomSoonja Yeom, Kim, P
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.


Publication title

Proceedings of the 34th Annual ACM Symposium on Applied Computing






School of Information and Communication Technology


Association for Computing Machinery

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Event title

34th Annual ACM Symposium on Applied Computing

Event Venue

Limassol, Cyprus

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Date of Event (End Date)


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Copyright the Authors

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  • Open

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Information systems, technologies and services not elsewhere classified

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