Plagiarism detection for Indonesian texts

As plagiarism becomes an increasing concern for Indonesian universities and research centers, the need of using automatic plagiarism checker is becoming more real. However, researches on Plagiarism Detection Systems (PDS) in Indonesian documents have not been well developed, since most of them deal with detecting duplicate or near-duplicate documents, have not addressed the problem of retrieving source documents, or show tendency to measure document similarity globally. Therefore, systems resulted from these researches are incapable of referring to exact locations of ``similar passage'' pairs. Besides, there has been no public and standard corpora available to evaluate PDS in Indonesian texts. To address the weaknesses of former researches, this thesis develops a plagiarism detection system which executes various methods of plagiarism detection stages in a workflow system. In retrieval stage, a novel document feature coined as phraseword is introduced and executed along with word unigram and character n-grams to address the problem of retrieving source documents, whose contents are copied partially or obfuscated in a suspicious document. The detection stage, which exploits a two-step paragraph-based comparison, is aimed to address the problems of detecting and locating source-obfuscated passage pairs. The seeds for matching source-obfuscated passage pairs are based on locally-weighted significant terms to capture paraphrased and summarized passages. In addition to this system, an evaluation corpus was created through simulation by human writers, and by algorithmic random generation. Using this corpus, the performance evaluation of the proposed methods was performed in three scenarios. On the first scenario which evaluated source retrieval performance, some methods using phraseword and token features were able to achieve the optimum recall rate 1. On the second scenario which evaluated detection performance, our system was compared to Alvi's algorithm and evaluated in 4 levels of measures: character, passage, document, and cases. The experiment results showed that methods resulted from using token as seeds have higher scores than Alvi's algorithm in all 4 levels of measures both in artificial and simulated plagiarism cases. In case detection, our systems outperform Alvi's algorithm in recognizing copied, shaked, and paraphrased passages. However, Alvi's recognition rate on summarized passage is insignificantly higher than our system. The same tendency of experiment results were demonstrated on the third experiment scenario, only the precision rates of Alvi's algorithm in character and paragraph levels are higher than our system. The higher Plagdet scores produced by some methods in our system than Alvi's scores show that this study has fulfilled its objective in implementing a competitive state-of-the-art algorithm for detecting plagiarism in Indonesian texts. Being run at our test document corpus, Alvi's highest scores of recall, precision, Plagdet, and detection rate on no-plagiarism cases correspond to its scores when it was tested on PAN'14 corpus. Thus, this study has contributed in creating a standard evaluation corpus for assessing PDS for Indonesian documents. Besides, this study contributes in a source retrieval algorithm which introduces phrasewords as document features, and a paragraph-based text alignment algorithm which relies on two different strategies. One of them is to apply local-word weighting used in text summarization field to select seeds for both discriminating paragraph pair candidates and matching process. The proposed detection algorithm results in almost no multiple detection. This contributes to the strength of this algorithm

Overview of the 5th International Competition on Plagiarism Detection

Abstract This paper overviews 18 plagiarism detectors that have been evaluated within the fifth international competition on plagiarism detection at PAN 2013. We report on their performances for the two tasks source retrieval and text alignment of external plagiarism detection. Furthermore, we continue last year’s initiative to invite software submissions instead of run submissions, and, re-evaluate this year’s submissions on last year’s evaluation corpora and vice versa, thus demonstrating the benefits of software submissions in terms of reproducibility.Potthast, M.; Hagen, M.; Gollub, T.; Tippmann, M.; Kiesel, J.; Rosso, P.; Stamatatos, E.... (2013). Overview of the 5th International Competition on Plagiarism Detection. CLEF Conference on Multilingual and Multimodal Information Access Evaluation. 301-331. http://hdl.handle.net/10251/46635S30133

Overview of the 6th International Competition on Plagiarism Detection

[EN] This paper overviews 17 plagiarism detectors that have been evaluated within the sixth international competition on plagiarism detection at PAN 2014. We report on their performances for the two tasks source retrieval and text alignment of external plagiarism detection. For the third year in a row, we invite software submissions instead of run submissions for this task, which allows for cross-year evaluations. Moreover, we introduce new performance measures for text alignment to shed light on new aspects of detection performance.We thank the participating teams of this task for their devoted work. This paper was partially supported by the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action.Potthast, M.; Hagen, M.; Beyer, A.; Busse, M.; Tippmann, M.; Rosso, P.; Stein, B. (2014). Overview of the 6th International Competition on Plagiarism Detection. CEUR Workshop Proceedings. 1180:845-876. http://hdl.handle.net/10251/61151S845876118

Plagiarism detection for Indonesian texts

As plagiarism becomes an increasing concern for Indonesian universities and research centers, the need of using automatic plagiarism checker is becoming more real. However, researches on Plagiarism Detection Systems (PDS) in Indonesian documents have not been well developed, since most of them deal with detecting duplicate or near-duplicate documents, have not addressed the problem of retrieving source documents, or show tendency to measure document similarity globally. Therefore, systems resulted from these researches are incapable of referring to exact locations of ``similar passage'' pairs. Besides, there has been no public and standard corpora available to evaluate PDS in Indonesian texts. To address the weaknesses of former researches, this thesis develops a plagiarism detection system which executes various methods of plagiarism detection stages in a workflow system. In retrieval stage, a novel document feature coined as phraseword is introduced and executed along with word unigram and character n-grams to address the problem of retrieving source documents, whose contents are copied partially or obfuscated in a suspicious document. The detection stage, which exploits a two-step paragraph-based comparison, is aimed to address the problems of detecting and locating source-obfuscated passage pairs. The seeds for matching source-obfuscated passage pairs are based on locally-weighted significant terms to capture paraphrased and summarized passages. In addition to this system, an evaluation corpus was created through simulation by human writers, and by algorithmic random generation. Using this corpus, the performance evaluation of the proposed methods was performed in three scenarios. On the first scenario which evaluated source retrieval performance, some methods using phraseword and token features were able to achieve the optimum recall rate 1. On the second scenario which evaluated detection performance, our system was compared to Alvi's algorithm and evaluated in 4 levels of measures: character, passage, document, and cases. The experiment results showed that methods resulted from using token as seeds have higher scores than Alvi's algorithm in all 4 levels of measures both in artificial and simulated plagiarism cases. In case detection, our systems outperform Alvi's algorithm in recognizing copied, shaked, and paraphrased passages. However, Alvi's recognition rate on summarized passage is insignificantly higher than our system. The same tendency of experiment results were demonstrated on the third experiment scenario, only the precision rates of Alvi's algorithm in character and paragraph levels are higher than our system. The higher Plagdet scores produced by some methods in our system than Alvi's scores show that this study has fulfilled its objective in implementing a competitive state-of-the-art algorithm for detecting plagiarism in Indonesian texts. Being run at our test document corpus, Alvi's highest scores of recall, precision, Plagdet, and detection rate on no-plagiarism cases correspond to its scores when it was tested on PAN'14 corpus. Thus, this study has contributed in creating a standard evaluation corpus for assessing PDS for Indonesian documents. Besides, this study contributes in a source retrieval algorithm which introduces phrasewords as document features, and a paragraph-based text alignment algorithm which relies on two different strategies. One of them is to apply local-word weighting used in text summarization field to select seeds for both discriminating paragraph pair candidates and matching process. The proposed detection algorithm results in almost no multiple detection. This contributes to the strength of this algorithm

Detecting Cross-Language Plagiarism using Open Knowledge Graphs

Identifying cross-language plagiarism is challenging, especially for distant language pairs and sense-for-sense translations. We introduce the new multilingual retrieval model Cross-Language Ontology-Based Similarity Analysis (CL-OSA) for this task. CL-OSA represents documents as entity vectors obtained from the open knowledge graph Wikidata. Opposed to other methods, CL-OSA does not require computationally expensive machine translation, nor pre-training using comparable or parallel corpora. It reliably disambiguates homonyms and scales to allow its application toWebscale document collections. We show that CL-OSA outperforms state-of-the-art methods for retrieving candidate documents from five large, topically diverse test corpora that include distant language pairs like Japanese-English. For identifying cross-language plagiarism at the character level, CL-OSA primarily improves the detection of sense-for-sense translations. For these challenging cases, CL-OSA’s performance in terms of the well-established PlagDet score exceeds that of the best competitor by more than factor two. The code and data of our study are openly available

Source Retrieval for Plagiarism Detection

Plagiarism has become a serious problem mainly because of the electronically available documents. An online document retrieval is a weighty part of a modern anti-plagiarism tool. This paper describes an architecture and concepts of a real-world document retrieval system, which is a part of a general anti-plagiarism software. Up to date systems for plagiarism detection are discussed from the source retrieval perspective. The key approaches of source retrieval are compared. The system recommendations stem from design, implementation, and several years of operation experience of a nationwide plagiarism solution at Masaryk University in the Czech Republic. The design can be adapted to many situations. Proper usage of such systems contributes to the gradual improvement of the quality of student theses.Plagiarism has become a serious problem mainly because of the electronically available documents. An online document retrieval is a weighty part of a modern anti-plagiarism tool. This paper describes an architecture and concepts of a real-world document retrieval system, which is a part of a general anti-plagiarism software. Up to date systems for plagiarism detection are discussed from the source retrieval perspective. The key approaches of source retrieval are compared. The system recommendations stem from design, implementation, and several years of operation experience of a nationwide plagiarism solution at Masaryk University in the Czech Republic. The design can be adapted to many situations. Proper usage of such systems contributes to the gradual improvement of the quality of student theses

On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism

Barrón Cedeño, LA. (2012). On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16012Palanci

Scalable and Language-Independent Embedding-based Approach for Plagiarism Detection Considering Obfuscation Type: No Training Phase

[EN] The efficiency and scalability of plagiarism detection systems have become a major challenge due to the vast amount of available textual data in several languages over the Internet. Plagiarism occurs in different levels of obfuscation, ranging from the exact copy of original materials to text summarization. Consequently, designed algorithms to detect plagiarism should be robust to the diverse languages and different type of obfuscation in plagiarism cases. In this paper, we employ text embedding vectors to compare similarity among documents to detect plagiarism. Word vectors are combined by a simple aggregation function to represent a text document. This representation comprises semantic and syntactic information of the text and leads to efficient text alignment among suspicious and original documents. By comparing representations of sentences in source and suspicious documents, pair sentences with the highest similarity are considered as the candidates or seeds of plagiarism cases. To filter and merge these seeds, a set of parameters, including Jaccard similarity and merging threshold, are tuned by two different approaches: offline tuning and online tuning. The offline method, which is used as the benchmark, regulates a unique set of parameters for all types of plagiarism by several trials on the training corpus. Experiments show improvements in performance by considering obfuscation type during threshold tuning. In this regard, our proposed online approach uses two statistical methods to filter outlier candidates automatically by their scale of obfuscation. By employing the online tuning approach, no distinct training dataset is required to train the system. We applied our proposed method on available datasets in English, Persian and Arabic languages on the text alignment task to evaluate the robustness of the proposed methods from the language perspective as well. As our experimental results confirm, our efficient approach can achieve considerable performance on the different datasets in various languages. Our online threshold tuning approach without any training datasets works as well as, or even in some cases better than, the training-base method.The work of Paolo Rosso was partially funded by the Spanish MICINN under the research Project MISMIS-FAKEn-HATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31).Gharavi, E.; Veisi, H.; Rosso, P. (2020). Scalable and Language-Independent Embedding-based Approach for Plagiarism Detection Considering Obfuscation Type: No Training Phase. Neural Computing and Applications. 32(14):10593-10607. https://doi.org/10.1007/s00521-019-04594-yS1059310607321