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

Plagiarism Detection Techniques for Arabic Script Languages: A Literature Review

Plagiarism is generally defined as literary theft and academic dishonesty. This considered as the serious issue in an academic documents and texts. There are numerous of plagiarism detection techniques have been developed for various natural languages, mainly English. In this paper we investigate and review the plagiarism detection techniques and algorithms which have been developed for Arabic Script Languages (ASL), and providing a literature review of the utilized methods in terms of techniques and outcomes.  The result of this paper will help the researchers who are going to commence their development and extend their researches in ASL like Arabic, Persian, Urdu, and Kurdish

A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning

Tesis por compendioNatural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human languages. One of its most challenging aspects involves enabling computers to derive meaning from human natural language. To do so, several meaning or context representations have been proposed with competitive performance. However, these representations still have room for improvement when working in a cross-domain or cross-language scenario. In this thesis we study the use of knowledge graphs as a cross-domain and cross-language representation of text and its meaning. A knowledge graph is a graph that expands and relates the original concepts belonging to a set of words. We obtain its characteristics using a wide-coverage multilingual semantic network as knowledge base. This allows to have a language coverage of hundreds of languages and millions human-general and -specific concepts. As starting point of our research we employ knowledge graph-based features - along with other traditional ones and meta-learning - for the NLP task of single- and cross-domain polarity classification. The analysis and conclusions of that work provide evidence that knowledge graphs capture meaning in a domain-independent way. The next part of our research takes advantage of the multilingual semantic network and focuses on cross-language Information Retrieval (IR) tasks. First, we propose a fully knowledge graph-based model of similarity analysis for cross-language plagiarism detection. Next, we improve that model to cover out-of-vocabulary words and verbal tenses and apply it to cross-language document retrieval, categorisation, and plagiarism detection. Finally, we study the use of knowledge graphs for the NLP tasks of community questions answering, native language identification, and language variety identification. The contributions of this thesis manifest the potential of knowledge graphs as a cross-domain and cross-language representation of text and its meaning for NLP and IR tasks. These contributions have been published in several international conferences and journals.El Procesamiento del Lenguaje Natural (PLN) es un campo de la informática, la inteligencia artificial y la lingüística computacional centrado en las interacciones entre las máquinas y el lenguaje de los humanos. Uno de sus mayores desafíos implica capacitar a las máquinas para inferir el significado del lenguaje natural humano. Con este propósito, diversas representaciones del significado y el contexto han sido propuestas obteniendo un rendimiento competitivo. Sin embargo, estas representaciones todavía tienen un margen de mejora en escenarios transdominios y translingües. En esta tesis estudiamos el uso de grafos de conocimiento como una representación transdominio y translingüe del texto y su significado. Un grafo de conocimiento es un grafo que expande y relaciona los conceptos originales pertenecientes a un conjunto de palabras. Sus propiedades se consiguen gracias al uso como base de conocimiento de una red semántica multilingüe de amplia cobertura. Esto permite tener una cobertura de cientos de lenguajes y millones de conceptos generales y específicos del ser humano. Como punto de partida de nuestra investigación empleamos características basadas en grafos de conocimiento - junto con otras tradicionales y meta-aprendizaje - para la tarea de PLN de clasificación de la polaridad mono- y transdominio. El análisis y conclusiones de ese trabajo muestra evidencias de que los grafos de conocimiento capturan el significado de una forma independiente del dominio. La siguiente parte de nuestra investigación aprovecha la capacidad de la red semántica multilingüe y se centra en tareas de Recuperación de Información (RI). Primero proponemos un modelo de análisis de similitud completamente basado en grafos de conocimiento para detección de plagio translingüe. A continuación, mejoramos ese modelo para cubrir palabras fuera de vocabulario y tiempos verbales, y lo aplicamos a las tareas translingües de recuperación de documentos, clasificación, y detección de plagio. Por último, estudiamos el uso de grafos de conocimiento para las tareas de PLN de respuesta de preguntas en comunidades, identificación del lenguaje nativo, y identificación de la variedad del lenguaje. Las contribuciones de esta tesis ponen de manifiesto el potencial de los grafos de conocimiento como representación transdominio y translingüe del texto y su significado en tareas de PLN y RI. Estas contribuciones han sido publicadas en diversas revistas y conferencias internacionales.El Processament del Llenguatge Natural (PLN) és un camp de la informàtica, la intel·ligència artificial i la lingüística computacional centrat en les interaccions entre les màquines i el llenguatge dels humans. Un dels seus majors reptes implica capacitar les màquines per inferir el significat del llenguatge natural humà. Amb aquest propòsit, diverses representacions del significat i el context han estat proposades obtenint un rendiment competitiu. No obstant això, aquestes representacions encara tenen un marge de millora en escenaris trans-dominis i trans-llenguatges. En aquesta tesi estudiem l'ús de grafs de coneixement com una representació trans-domini i trans-llenguatge del text i el seu significat. Un graf de coneixement és un graf que expandeix i relaciona els conceptes originals pertanyents a un conjunt de paraules. Les seves propietats s'aconsegueixen gràcies a l'ús com a base de coneixement d'una xarxa semàntica multilingüe d'àmplia cobertura. Això permet tenir una cobertura de centenars de llenguatges i milions de conceptes generals i específics de l'ésser humà. Com a punt de partida de la nostra investigació emprem característiques basades en grafs de coneixement - juntament amb altres tradicionals i meta-aprenentatge - per a la tasca de PLN de classificació de la polaritat mono- i trans-domini. L'anàlisi i conclusions d'aquest treball mostra evidències que els grafs de coneixement capturen el significat d'una forma independent del domini. La següent part de la nostra investigació aprofita la capacitat\hyphenation{ca-pa-ci-tat} de la xarxa semàntica multilingüe i se centra en tasques de recuperació d'informació (RI). Primer proposem un model d'anàlisi de similitud completament basat en grafs de coneixement per a detecció de plagi trans-llenguatge. A continuació, vam millorar aquest model per cobrir paraules fora de vocabulari i temps verbals, i ho apliquem a les tasques trans-llenguatges de recuperació de documents, classificació, i detecció de plagi. Finalment, estudiem l'ús de grafs de coneixement per a les tasques de PLN de resposta de preguntes en comunitats, identificació del llenguatge natiu, i identificació de la varietat del llenguatge. Les contribucions d'aquesta tesi posen de manifest el potencial dels grafs de coneixement com a representació trans-domini i trans-llenguatge del text i el seu significat en tasques de PLN i RI. Aquestes contribucions han estat publicades en diverses revistes i conferències internacionals.Franco Salvador, M. (2017). A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84285TESISCompendi

Mono- and cross-lingual paraphrased text reuse and extrinsic plagiarism detection

Text reuse is the act of borrowing text (either verbatim or paraphrased) from an earlier written text. It could occur within the same language (mono-lingual) or across languages (cross-lingual) where the reused text is in a different language than the original text. Text reuse and its related problem, plagiarism (the unacknowledged reuse of text), are becoming serious issues in many fields and research shows that paraphrased and especially the cross-lingual cases of reuse are much harder to detect. Moreover, the recent rise in readily available multi-lingual content on the Web and social media has increased the problem to an unprecedented scale. To develop, compare, and evaluate automatic methods for mono- and crosslingual text reuse and extrinsic (finding portion(s) of text that is reused from the original text) plagiarism detection, standard evaluation resources are of utmost importance. However, previous efforts on developing such resources have mostly focused on English and some other languages. On the other hand, the Urdu language, which is widely spoken and has a large digital footprint, lacks resources in terms of core language processing tools and corpora. With this consideration in mind, this PhD research focuses on developing standard evaluation corpora, methods, and supporting resources to automatically detect mono-lingual (Urdu) and cross-lingual (English-Urdu) cases of text reuse and extrinsic plagiarism This thesis contributes a mono-lingual (Urdu) text reuse corpus (COUNTER Corpus) that contains real cases of Urdu text reuse at document-level. Another contribution is the development of a mono-lingual (Urdu) extrinsic plagiarism corpus (UPPC Corpus) that contains simulated cases of Urdu paraphrase plagiarism. Evaluation results, by applying a wide range of state-of-the-art mono-lingual methods on both corpora, shows that it is easier to detect verbatim cases than paraphrased ones. Moreover, the performance of these methods decreases considerably on real cases of reuse. A couple of supporting resources are also created to assist methods used in the cross-lingual (English-Urdu) text reuse detection. A large-scale multi-domain English-Urdu parallel corpus (EUPC-20) that contains parallel sentences is mined from the Web and several bi-lingual (English-Urdu) dictionaries are compiled using multiple approaches from different sources. Another major contribution of this study is the development of a large benchmark cross-lingual (English-Urdu) text reuse corpus (TREU Corpus). It contains English to Urdu real cases of text reuse at the document-level. A diversified range of methods are applied on the TREU Corpus to evaluate its usefulness and to show how it can be utilised in the development of automatic methods for measuring cross-lingual (English-Urdu) text reuse. A new cross-lingual method is also proposed that uses bilingual word embeddings to estimate the degree of overlap amongst text documents by computing the maximum weighted cosine similarity between word pairs. The overall low evaluation results indicate that it is a challenging task to detect crosslingual real cases of text reuse, especially when the language pairs have unrelated scripts, i.e., English-Urdu. However, an improvement in the result is observed using a combination of methods used in the experiments. The research work undertaken in this PhD thesis contributes corpora, methods, and supporting resources for the mono- and cross-lingual text reuse and extrinsic plagiarism for a significantly under-resourced Urdu and English-Urdu language pair. It highlights that paraphrased and cross-lingual cross-script real cases of text reuse are harder to detect and are still an open issue. Moreover, it emphasises the need to develop standard evaluation and supporting resources for under-resourced languages to facilitate research in these languages. The resources that have been developed and methods proposed could serve as a framework for future research in other languages and language pairs

A Contextual Topic Modeling and Content Analysis of Iranian laws and Regulations

A constitution is the highest legal document of a country and serves as a guide for the establishment of other laws. The constitution defines the political principles, structure, hierarchy, position, and limits of the political power of a country's government. It determines and guarantees the rights of citizens. This study aimed at topic modeling of Iranian laws. As part of this research, 11760 laws were collected from the Dotic website. Then, topic modeling was conducted on the title and content of the regularizations using LDA. Data analysis with topic modeling led to the identification of 10 topics including Economic, Customs, Housing and Urban Development, Agriculture, Insurance, Legal and judicial, Cultural, Information Technology, Political, and Government. The largest topic, Economic, accounts for 29% of regulations, while the smallest are Political and Government, accounting for 2%. This research utilizes a topic modeling method in exploring law texts and identifying trends in regularizations from 2016-2023. In this study, it was found that regularizations constitute a significant percentage of law, most of which are related to economics and customs. Cultural regularizations have increased in 2023. It can be concluded any law enacted each year can reflect society's conditions and legislators' top concerns

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Peer reviewe

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis