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Semantic analysis for paraphrase identification using semantic role labeling


Lee, E and Lynn, HM and Kim, HJ and Yeom, S and Kim, P, Semantic analysis for paraphrase identification using semantic role labeling, Proceedings of the 34th Annual ACM Symposium on Applied Computing, 8-12 April 2019, Limassol, Cyprus, pp. 2135-2138. ISBN 9781450359337 (2019) [Refereed Conference Paper]


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DOI: doi:10.1145/3297280.3300200


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 Details

Item Type:Refereed Conference Paper
Keywords:text reuse, text similarity, semantic role labeling, PAN 2012 corpus
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Natural language processing
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Yeom, S (Dr Soonja Yeom)
ID Code:132078
Year Published:2019
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2019-04-18
Last Modified:2022-05-23
Downloads:36 View Download Statistics

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