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Fine-Grained Analysis of Language Varieties and Demographics
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Multilingual Stance Detection in Social Media Political Debates
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Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
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Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection
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The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
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Scalable and Language-Independent Embedding-based Approach for Plagiarism Detection Considering Obfuscation Type: No Training Phase
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Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions
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Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter
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IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
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On the Use of Character n-grams as the only Intrinsic Evidence of Plagiarism
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Online Hate Speech against Women: Automatic Identification of Misogyny and Sexism on Twitter
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On the use of word embedding for cross language plagiarism detection
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Overview of PAN 2019: Bots and Gender Profiling, Celebrity Profiling, Cross-domain Authorship Attribution and Style Change Detection
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Improving Attitude Words Classification for Opinion Mining using Word Embedding
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Classifier combination approach for question classification for Bengali question answering system
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Paraphrase Plagiarism Identifcation with Character-level Features
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Abstract:
[EN] Several methods have been proposed for determining plagiarism between pairs of sentences, passages or even full documents. However, the majority of these methods fail to reliably detect paraphrase plagiarism due to the high complexity of the task, even for human beings. Paraphrase plagiarism identi cation consists in automatically recognizing document fragments that contain re-used text, which is intentionally hidden by means of some rewording practices such as semantic equivalences, discursive changes, and morphological or lexical substitutions. Our main hypothesis establishes that the original author's writing style ngerprint prevails in the plagiarized text even when paraphrases occur. Thus, in this paper we propose a novel text representation scheme that gathers both content and style characteristics of texts, represented by means of character-level features. As an additional contribution, we describe the methodology followed for the construction of an appropriate corpus for the task of paraphrase plagiarism identi cation, which represents a new valuable resource to the NLP community for future research work in this field. ; This work is the result of the collaboration in the framework of the CONACYT Thematic Networks program (RedTTL Language Technologies Network) and the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action. The first author was supported by CONACYT (Scholarship 258345/224483). The second, third, and sixth authors were partially supported by CONACyT (Project Grants 258588 and 2410). The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the Grant ALMAMATER (PrometeoII/2014/030). ; Sánchez-Vega, F.; Villatoro-Tello, E.; Montes-Y-Gómez, M.; Rosso, P.; Stamatatos, E.; Villaseñor-Pineda, L. (2019). Paraphrase Plagiarism Identifcation with Character-level Features. Pattern Analysis and Applications. 22(2):669-681. https://doi.org/10.1007/s10044-017-0674-z ; S ; 669 ; 681 ; 22 ; 2 ; Barrón-Cedeño A, Rosso P (2009) On automatic plagiarism detection based on n-grams comparison. In: Proceedings of the 31th European conference on IR research on advances in information retrieval (ECIR), LNCS vol 5478, Springer, Berlin, pp 696–700 ; Barron-Cedeño A, Vila M, Martí MA, Rosso P (2013) Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection. Comput Linguist 39(4):917–947 ; Basile C, Benedetto D, Caglioti E, Cristadoro G, Esposti M (2009) A plagiarism detection procedure in three steps: selection, matches and “squares”. In: Proceedings of the SEPLN 2009 workshop on uncovering plagiarism, authorship and social software misuse (PAN 2009), CEUR-WS vol 502. Donostia-San Sebastian, Spain ; Biggins S, Mohammed S, Oakley S (2012) University of shefield: two approaches to semantic text similarity. In: First joint conference on lexical and computational semantics (SEM at NAACL 2012), Montreal, Canada, pp 655–661 ; Burrows S, Potthast M, Stein B (2013) Paraphrase acquisition via crowdsourcing and machine learning. ACM Trans Intell Syst Technol 4(3):43:1–43:21. https://doi.org/10.1145/2483669.2483676 ; Calvo H, Segura-Olivares A, García A (2014) Dependency vs. constituent based syntactic n-grams in text similarity measures for paraphrase recognition. Computación y Sistemas 18(3):517554 ; Chien-Ying C, Jen-Yuan Y, Hao-Ren K (2010) Plagiarism detection using rouge and wordnet. J Comput 2(3):34–44 ; Chong M, Specia L, Mitkov R (2010) Using natural language processing for automatic detection of plagiarism. In: Proceedings of the 4th international plagiarism conference. Newcastle-upon-Tyne, UK ; Clough P (2003) Old a new challenges in automatic plagiarism detection. In: National plagiarism advisory service, pp 391–407 ; Clough P, Gaizauskas R, Piao SS, Wilks Y (2002) Meter: Measuring text reuse. In: Proceedings of the 40th annual meeting of the association for computational linguistics (ACL). Philadelphia ; Courtney C, Mihalcea R (2005) Measuring the semantic similarity of texts. In: Proceedings of the ACL workshop on empirical modeling of semantic equivalence and entailment (EMSEE at NAALC 2005), pp 13–18 ; Daelemans W (2013) Explanation in computational stylometry. In: 14th International conference on intelligent text processing and computational linguistics (CIC-Ling 2013), Lecture Notes in Computer Science LNCS, vol 7817, pp 451–462 ; Ehsan N, Shakery A (2016) Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information. Inf Process Manag. https://doi.org/10.1016/j.ipm.2016.04.006 ; Grieve J (2007) Quantitative authorship attribution: an evaluation of techniques. Lit Linguist Comput 22(3):251–270 ; Hartrumpf S, vor Der Brück T, Eichhorn C (2010) Semantic duplicate identification with parsing and machine learning. In: Eleventh international conference on text, speech and dialogue (TSD 2010) LNAI vol 6231, Springer, Berlin, pp 84–92. Brno, Czech Republic ; Hoad TC, Zobel J (2003) Methods for identifying versioned and plagiarised documents. J Am Soc Inform Sci Technol 54:203–215 ; Koppel M, Schler J, Argamon S (2009) Computational methods in authorship attribution. J Am Soc Inf Sci Technol 60(1):9–26 ; Koppel M, Schler J, Argamon S (2011) Authorship attribution in the wild. Lang Resour Eval 45:83–94 ; Man PD (1983) Blindness and insight: essays in the rhetoric of contemporary criticism, 2nd ed. chap. Literature and Language: A Commentary, pp. 277–89. Routtloedge ; McNamee P, Mayfield J (2004) Character n-gram tokenization for european language text retrieval. Inf Retr 7(1–2):73–97 ; Oberreuter G, L’Huillier G, Ríos SA, Velásquez JD (2011) Approaches for intrinsic and external plagiarism detection. In: Notebook for PAN at CLEF’11 ; Palkovskii Y, Belov A, Muzyka I (2011) Using wordnet-based semantic similarity measurement in external plagiarism detection. In: Notebook for PAN at CLEF’11 ; Potthast M, Hagen M, Gollub T, Tippmann M, Kiesel J, Rosso P, Stamatatos E, Stein B (2013) Overview of the 5th international competition on plagiarism detection. In: CLEF 2013 evaluation labs and workshop working notes papers ; Ravi NR, Gupta D (2015) Efficient paragraph based chunking and download filtering for plagiarism source retrieval. In: Notebook for PAN at CLEF 2015 evaluation labs and workshop working notes papers, PAN ’15. http://www.uni-weimar.de/medien/webis/events/pan-15/pan15-papers-final/pan15-plagiarism-detection/ravi15-notebook.pdf ; Sapkota U, Bethard S, Montes-y Gómez M, Solorio T (2015) Not all character n-grams are created equal: a study in authorship attribution. In: Conference of the North American chapter of the association for computational linguistics human language technologies (NAACL-HLT 2015), pp 93–102 ; Sapkota U, Solorio T, Montes M, Bethard S, Rosso P (2014) Cross-topic authorship attribution: will out-of-topic data help? In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pp 1228–1237. Dublin City University and Association for Computational Linguistics. http://aclweb.org/anthology/C14-1116 ; Schleimer S, Wilkerson DS, Aiken A (2003) Winnowing: local algorithms for document fingerprinting. In: Proceedings of the 2003 ACM SIGMOD international conference on management of data, SIGMOD ’03, pp 76–85. ACM, New York. https://doi.org/10.1145/872757.872770 ; Sediyono A, Mahamud K (2008) Algorithm of the longest commonly consecutive word for plagiarism detection in text based document. In: Digital information management, ICDIM ’08, pp 253–259. IEEE. https://doi.org/10.1109/ICDIM.2008.4746827 ; Shivakumar N, Garcia-Molina H (1995) Scam: a copy detection mechanism for digital documents. In: Proceedings of the second annual conference on the theory and practice of digital libraries ; Si A, Leong HV, Lau RWH (1997) Check: a document plagiarism detection system. In: Proceedings of ACM symposium for applied computing, SAC ’97, pp. 70–77. ACM, New York. https://doi.org/10.1145/331697.335176 ; Sánchez-Vega F, Villatoro-Tello E, Montes-y Gómez M, Villaseñor-Pineda L, Rosso P (2013) Determining and characterizing the reused text for plagiarism detection. Expert Syst Appl 40(5):1804–1813 ; Stamatatos E (2011) Plagiarism detection using stopword n-grams. J Am Soc Inf Sci Technol 62(12):2512–2527 ; Stamatatos E (2013) On the robustness of authorship attribution based on character n-gram features. J Law Policy 21(2):421–439 ; Stein B, Potthast M, Rosso P, Barrón-Cedeño A, Stamatatos E, Koppel M (2011) Fourth international workshop on uncovering plagiarism, authorship, and social software misuse. SIGIR Forum 45:45–48 ; Uzuner Özlem, Katz B, Nahnsen T (2005) Using syntactic information to identify plagiarism. In: Proceedings of 2nd workshop on building educational applications using NLP. Ann Arbor ; Xu W, Ritter A, Dolan WB, Grishman R, Cherry C (2012) Paraphrasing for style. In: Proceedings of COLING 2012: Technical Papers, pp 2899–2914. Mumbai ; Zechner M, Muhr M, Kern R, Granitzer M (2009) External and intrinsic plagiarism detection using vector space models. In: SEPLN 2009, workshop on uncovering plagiarism, authorship, and social software misuse (PAN 09), pp 45–55
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Keyword:
Character n-grams; LENGUAJES Y SISTEMAS INFORMATICOS; Paraphrase plagiarism; Plagiarism identification; Stylistic representation; Text reuse
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URL: http://hdl.handle.net/10251/159992
https://doi.org/10.1007/s10044-017-0674-z
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| 40 |
A Low Dimensionality Representation for Language Variety Identification
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