117 research outputs found

    Plagiarism detection for Indonesian texts

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    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 6th International Competition on Plagiarism Detection

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    [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

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

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    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

    Overview of the 5th International Competition on Plagiarism Detection

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    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

    Plagiarism detection for Indonesian texts

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    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

    An IR-based Approach Utilising Query Expansion for Plagiarism Detection in MEDLINE

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    The identification of duplicated and plagiarised passages of text has become an increasingly active area of research. In this paper we investigate methods for plagiarism detection that aim to identify potential sources of plagiarism from MEDLINE, particularly when the original text has been modified through the replacement of words or phrases. A scalable approach based on Information Retrieval is used to perform candidate document selection - the identification of a subset of potential source documents given a suspicious text - from MEDLINE. Query expansion is performed using the ULMS Metathesaurus to deal with situations in which original documents are obfuscated. Various approaches to Word Sense Disambiguation are investigated to deal with cases where there are multiple Concept Unique Identifiers (CUIs) for a given term. Results using the proposed IR-based approach outperform a state-of-the-art baseline based on Kullback-Leibler Distance

    Technologies for Reusing Text from the Web

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    Texts from the web can be reused individually or in large quantities. The former is called text reuse and the latter language reuse. We first present a comprehensive overview of the different ways in which text and language is reused today, and how exactly information retrieval technologies can be applied in this respect. The remainder of the thesis then deals with specific retrieval tasks. In general, our contributions consist of models and algorithms, their evaluation, and for that purpose, large-scale corpus construction. The thesis divides into two parts. The first part introduces technologies for text reuse detection, and our contributions are as follows: (1) A unified view of projecting-based and embedding-based fingerprinting for near-duplicate detection and the first time evaluation of fingerprint algorithms on Wikipedia revision histories as a new, large-scale corpus of near-duplicates. (2) A new retrieval model for the quantification of cross-language text similarity, which gets by without parallel corpora. We have evaluated the model in comparison to other models on many different pairs of languages. (3) An evaluation framework for text reuse and particularly plagiarism detectors, which consists of tailored detection performance measures and a large-scale corpus of automatically generated and manually written plagiarism cases. The latter have been obtained via crowdsourcing. This framework has been successfully applied to evaluate many different state-of-the-art plagiarism detection approaches within three international evaluation competitions. The second part introduces technologies that solve three retrieval tasks based on language reuse, and our contributions are as follows: (4) A new model for the comparison of textual and non-textual web items across media, which exploits web comments as a source of information about the topic of an item. In this connection, we identify web comments as a largely neglected information source and introduce the rationale of comment retrieval. (5) Two new algorithms for query segmentation, which exploit web n-grams and Wikipedia as a means of discerning the user intent of a keyword query. Moreover, we crowdsource a new corpus for the evaluation of query segmentation which surpasses existing corpora by two orders of magnitude. (6) A new writing assistance tool called Netspeak, which is a search engine for commonly used language. Netspeak indexes the web in the form of web n-grams as a source of writing examples and implements a wildcard query processor on top of it.Texte aus dem Web können einzeln oder in großen Mengen wiederverwendet werden. Ersteres wird Textwiederverwendung und letzteres Sprachwiederverwendung genannt. Zunächst geben wir einen ausführlichen Überblick darüber, auf welche Weise Text und Sprache heutzutage wiederverwendet und wie Technologien des Information Retrieval in diesem Zusammenhang angewendet werden können. In der übrigen Arbeit werden dann spezifische Retrievalaufgaben behandelt. Unsere Beiträge bestehen dabei aus Modellen und Algorithmen, ihrer empirischen Auswertung und der Konstruktion von großen Korpora hierfür. Die Dissertation ist in zwei Teile gegliedert. Im ersten Teil präsentieren wir Technologien zur Erkennung von Textwiederverwendungen und leisten folgende Beiträge: (1) Ein Überblick über projektionsbasierte- und einbettungsbasierte Fingerprinting-Verfahren für die Erkennung nahezu identischer Texte, sowie die erstmalige Evaluierung einer Reihe solcher Verfahren auf den Revisionshistorien der Wikipedia. (2) Ein neues Modell zum sprachübergreifenden, inhaltlichen Vergleich von Texten. Das Modell basiert auf einem mehrsprachigen Korpus bestehend aus Pärchen themenverwandter Texte, wie zum Beispiel der Wikipedia. Wir vergleichen das Modell in mehreren Sprachen mit herkömmlichen Modellen. (3) Eine Evaluierungsumgebung für Algorithmen zur Plagiaterkennung. Die Umgebung besteht aus Maßen, die die Güte der Erkennung eines Algorithmus' quantifizieren, und einem großen Korpus von Plagiaten. Die Plagiate wurden automatisch generiert sowie mit Hilfe von Crowdsourcing manuell erstellt. Darüber hinaus haben wir zwei Workshops veranstaltet, in denen unsere Evaluierungsumgebung erfolgreich zur Evaluierung aktueller Plagiaterkennungsalgorithmen eingesetzt wurde. Im zweiten Teil präsentieren wir auf Sprachwiederverwendung basierende Technologien für drei verschiedene Retrievalaufgaben und leisten folgende Beiträge: (4) Ein neues Modell zum medienübergreifenden, inhaltlichen Vergleich von Objekten aus dem Web. Das Modell basiert auf der Auswertung der zu einem Objekt vorliegenden Kommentare. In diesem Zusammenhang identifizieren wir Webkommentare als eine in der Forschung bislang vernachlässigte Informationsquelle und stellen die Grundlagen des Kommentarretrievals vor. (5) Zwei neue Algorithmen zur Segmentierung von Websuchanfragen. Die Algorithmen nutzen Web n-Gramme sowie Wikipedia, um die Intention des Suchenden in einer Suchanfrage festzustellen. Darüber hinaus haben wir mittels Crowdsourcing ein neues Evaluierungskorpus erstellt, das zwei Größenordnungen größer ist als bisherige Korpora. (6) Eine neuartige Suchmaschine, genannt Netspeak, die die Suche nach gebräuchlicher Sprache ermöglicht. Netspeak indiziert das Web als Quelle für gebräuchliche Sprache in der Form von n-Grammen und implementiert eine Wildcardsuche darauf

    Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

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    Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen
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