Detecting automatically generated sentences with grammatical structure similarity

Nguyen Minh Tien () and Cyril Labbé ()
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Nguyen Minh Tien: Univ. Grenoble Alpes
Cyril Labbé: Univ. Grenoble Alpes

Scientometrics, 2018, vol. 116, issue 2, No 31, 1247-1271

Abstract: Abstract Automatically generated papers have been used to manipulate bibliography indexes on numerous occasions. This paper is interested in different means to generate texts such as recurrent neural network, Markov model, or probabilistic context free grammar, and if it is possible to detect them using a current approach. Then, probabilistic context free grammar (PCFG) is focused on as the one most used. However, even though there have been multiple approaches to detect such types of paper, they are all working at the document level and are unable to detect a small amount of generated text inside a larger body of genuinely written text. Thus, we present the grammatical structure similarity measurement to detect sentences or short fragments of automatically generated text from known PCFG generators. The proposed approach is tested against a pattern checker and various common machine learning methods. Additionally, the ability to detect a modified PCFG generator is also tested.

Keywords: Automatically generated text; Bibliography manipulation; Markov model; Recurrent neural network; Probabilistic context free grammar; Grammatical structure (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11192-018-2789-4

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