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Статті в журналах з теми "Paraphrase Detection"
Altheneyan, Alaa, and Mohamed El Bachir Menai. "Evaluation of State-of-the-Art Paraphrase Identification and Its Application to Automatic Plagiarism Detection." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 04 (August 22, 2019): 2053004. http://dx.doi.org/10.1142/s0218001420530043.
Повний текст джерелаZhou, Ying, Xiaokang Hu, and Vera Chung. "Automatic Construction of Fine-Grained Paraphrase Corpora System Using Language Inference Model." Applied Sciences 12, no. 1 (January 5, 2022): 499. http://dx.doi.org/10.3390/app12010499.
Повний текст джерелаSiswantining, Titin, Stanley Pratama, and Devvi Sarwinda. "SPRATAMA MODEL FOR INDONESIAN PARAPHRASE DETECTION USING BIDIRECTIONAL LONG SHORT-TERM MEMORY AND BIDIRECTIONAL GATED RECURRENT UNIT." MEDIA STATISTIKA 15, no. 2 (March 5, 2023): 129–38. http://dx.doi.org/10.14710/medstat.15.2.129-138.
Повний текст джерелаBarrón-Cedeño, Alberto, Marta Vila, M. Martí, and Paolo Rosso. "Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection." Computational Linguistics 39, no. 4 (December 2013): 917–47. http://dx.doi.org/10.1162/coli_a_00153.
Повний текст джерелаBamnote, Dr G. R., and Ms Deepti Ingole. "Design of Efficient Model to Predict Duplications in Questionnaire Forum using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5893–97. http://dx.doi.org/10.22214/ijraset.2023.53088.
Повний текст джерелаChitra, A., and Anupriya Rajkumar. "Plagiarism Detection Using Machine Learning-Based Paraphrase Recognizer." Journal of Intelligent Systems 25, no. 3 (July 1, 2016): 351–59. http://dx.doi.org/10.1515/jisys-2014-0146.
Повний текст джерелаVrbanec, Tedo, and Ana Meštrović. "Corpus-Based Paraphrase Detection Experiments and Review." Information 11, no. 5 (April 29, 2020): 241. http://dx.doi.org/10.3390/info11050241.
Повний текст джерелаHudson, G. Thomas, and Noura Al Moubayed. "Ask me in your own words: paraphrasing for multitask question answering." PeerJ Computer Science 7 (October 27, 2021): e759. http://dx.doi.org/10.7717/peerj-cs.759.
Повний текст джерелаKumova Metin, Senem, Bahar Karaoğlan, Tarık Kışla, and Katira Soleymanzadeh. "Certainty factor model in paraphrase detection." Pamukkale University Journal of Engineering Sciences 27, no. 2 (2021): 139–50. http://dx.doi.org/10.5505/pajes.2020.75350.
Повний текст джерелаKong, Leilei, Zhongyuan Han, Yong Han, and Haoliang Qi. "A Deep Paraphrase Identification Model Interacting Semantics with Syntax." Complexity 2020 (October 30, 2020): 1–14. http://dx.doi.org/10.1155/2020/9757032.
Повний текст джерелаДисертації з теми "Paraphrase Detection"
Mayes, Robin James. "A Content Originality Analysis of HRD Focused Dissertations and Published Academic Articles using TurnItIn Plagiarism Detection Software." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984233/.
Повний текст джерелаVila, Rigat Marta. "Paraphrase Scope and Typology. A Data-Driven Approach from Computational Linguistics / Abast i tipologia de la paràfrasi. Una aproximació empíriica des de la lingüíística computacional." Doctoral thesis, Universitat de Barcelona, 2013. http://hdl.handle.net/10803/117850.
Повний текст джерелаS'entén per paràfrasi la igualtat aproximada de significat entre fragments de text que difereixen en la forma. La paràfrasi és omnipresent en les llengües naturals, on es troba expressada de múltiples maneres. D'una banda, la ubiqüitat de la paràfrasi l'ha convertit en el centre d’interès de moltes tasques específiques dins de la lingüística computacional; de l'altra, la seva complexitat ha fet de la paràfrasi un problema que encara no té una solució definitiva. Dues qüestions bàsiques, lligades a la naturalesa complexa de la paràfrasi, fan el seu tractament computacional particularment difícil: l'absència d'una definició precisa i comunament acceptada i la manca de corpus de paràfrasis de referència. Assumint que el coneixement lingüístic ha de ser a la base de la recerca en lingüística computacional, aquesta tesi pretén avançar en dues línies de treball: en la delimitació i comprensió del que s’entén per paràfrasi, i en la creació i anotació de corpus de paràfrasis que proporcionin dades sobre les quals fonamentar tant la recerca com futurs recursos i aplicacions. Amb l'objectiu d’avaluar empíricament el seu potencial, el coneixement i els recursos creats com a resultat d'aquest treball han estat aplicats a la detecció automàtica de plagi. Aquesta tesi consisteix en un compendi de publicacions i comprèn sis articles principals dividits en tres blocs: (i) abast i tipologia de la paràfrasi, (ii) creació i anotació de corpus de paràfrasis i (iii) la paràfrasi en la detecció automàtica de plagi. En el primer bloc, partint de la base que els límits de la paràfrasi no són fixos, sinó que depenen de l'àrea de treball, la tasca i els objectius, es presenten tres casos límit de la paràfrasi: la pèrdua de contingut, el coneixement pragmàtic i la variació en determinats trets gramaticals. La caracterització de la paràfrasi pren una nova dimensió si l'observem des d'una perspectiva extensional. En aquesta línia, s'ha construït una tipologia general de la paràfrasi lingüísticament fonamentada. La tercera qüestió tractada en aquest bloc és la representació de la paràfrasi, essencial a l'hora de tractar-la formalment. En el segon bloc, es presenta un mètode per a l’adquisició de paràfrasis relacionals a partir de la Wikipedia (Wikipedia-based Relational Paraphrase Acquistion, WRPA). Aquest mètode permet extreure automàticament de la Wikipedia paràfrasis que expressen una relació concreta. Utilitzant aquest mètode, s'ha creat el corpus WRPA, que cobreix diverses relacions i dues llengües (anglès i espanyol). Un subconjunt del corpus WRPA en espanyol i exemples extrets de dos corpus de paràfrasis en anglès s'han anotat amb els tipus de paràfrasis que es proposen en aquesta tesi. Aquesta anotació ha estat validada aplicant les mesures d’acord entre anotadors (Inter-annotator Agreement for Paraphrase-Type Annotation, IAPTA), també desenvolupades en el marc d'aquesta tesi. En el tercer i últim bloc, la tipologia proposada s'ha aplicat a l'àmbit de la detecció automàtica de plagi i s'ha demostrat que els tipus de paràfrasis més complexos i l'alta concentració de mecanismes de paràfrasi fan més difícil la detecció del plagi. També s'ha demostrat que les substitucions lèxiques i l'addició/eliminació de fragments de text són els mecanismes de paràfrasi més utilitzats en el plagi. Així, es demostra el potencial del coneixement parafràstic en la detecció automàtica de plagi i en la recerca en lingüística computacional en general.
Nawab, Rao Muhammad Adeel. "Mono-lingual paraphrased text reuse and plagiarism detection." Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/2785/.
Повний текст джерелаKumar, Ashutosh. "Inducing Constraints in Paraphrase Generation and Consistency in Paraphrase Detection." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6055.
Повний текст джерелаMaraev, Vladislav. "Modelling semantic relations with distributitional semantics and deep learning : question answering, entailment recognition and paraphrase detection." Master's thesis, 2017. http://hdl.handle.net/10451/30183.
Повний текст джерелаThis dissertation presents an approach to the task of modelling semantic relations between two texts, which is based on distributional semantic models and deep learning. The present work takes advantage of various disciplines of cognitive science, mainly computation, linguistics and artificial intelligence, with strong influences from neuroscience and cognitive psychology. Distributional semantic models (also known as word embeddings) are used to represent the meaning of words. Word semantic representations can be further combined towards obtaining the meaning of a larger chunk of a text using a deep learning approach, namely with the support of convolutional neural networks. These approaches are used to replicate the experiment carried out, by Bogdanova et al. (2015), for the task of detecting questions that can be answered by exactly the same answer in online user forums. Performance results obtained by my experiments are comparable or better than the ones reported in that referenced work. I present also a study on the impact of appropriate text preprocessing with respect to the results that can be obtained by the approaches adopted in that referenced work. Removing certain clues that can unduly help the system to detect equivalent questions leads to a significant decrease in system’s performance supported by that referenced work. I also present a study of the impact that pre-trained word embeddings have in the task of detecting the semantically equivalent questions. Replacing pre-trained word embeddings by randomly initialised ones improves the performance of the system. Additionally, the model was applied to the task of entailment recognition for Portuguese and showed an accuracy on a level with the baseline. This dissertation also reports on the results of an experimental study on the application of the adopted approach to the shared task of sentence paraphrase detection in Russian. The final set up contained two improvements: it uses several convolutional filters and it uses character embeddings instead of word embeddings. It was tested in Task 2 standard run of the relevant shared task and it showed competitive results.
Han, Nai-Hsuan, and 韓乃軒. "A Study of Selecting Machine Learning Features for Detecting Entailment, Paraphrase and Contradiction in Texts." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/44594957024872746237.
Повний текст джерела國立雲林科技大學
資訊工程系碩士班
101
NTCIR-9 RITE task evaluates systems which automatically detect entailment, paraphrase, and contradiction in texts. We developed a preliminary system for the NTCIR-9 RITE task based on rules. In NTCIR-10, we tried machine learning approaches. We transformed the existing rules into features and then added additional syntactic and semantic features for SVM. The straightforward assumption was still kept in NTCIR-10: the relation between two sentences was determined by the different parts between them instead of the identical parts. Therefore, features in NTCIR-9 including sentence lengths, the content of matched keywords, quantities of matched keywords, and their parts of speech together with new features such as parsing tree information, dependency relations, negation words and synonyms were considered. We found that some features were useful for the BC subtask while some help more in the MC subtask.
Частини книг з теми "Paraphrase Detection"
Tian, Liuyang, Hui Ning, Leilei Kong, Kaisheng Chen, Haoliang Qi, and Zhongyuan Han. "Sentence Paraphrase Detection Using Classification Models." In Text Processing, 166–81. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73606-8_13.
Повний текст джерелаSenthil Kumar, B., D. Thenmozhi, and S. Kayalvizhi. "Tamil Paraphrase Detection Using Encoder-Decoder Neural Networks." In IFIP Advances in Information and Communication Technology, 30–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63467-4_3.
Повний текст джерелаFujita, Atsushi, Kentaro Inui, and Yuji Matsumoto. "Detection of Incorrect Case Assignments in Paraphrase Generation." In Natural Language Processing – IJCNLP 2004, 555–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_59.
Повний текст джерелаDeléger, Louise, Bruno Cartoni, and Pierre Zweigenbaum. "Paraphrase Detection in Monolingual Specialized/Lay Comparable Corpora." In Building and Using Comparable Corpora, 223–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-20128-8_12.
Повний текст джерелаThenmozhi, Durairaj, C. Jerin Mahibha, S. Kayalvizhi, M. Rakesh, Y. Vivek, and V. Poojesshwaran. "Paraphrase Detection in Indian Languages Using Deep Learning." In Communications in Computer and Information Science, 138–54. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-33231-9_9.
Повний текст джерелаMahmoud, Adnen, Ahmed Zrigui, and Mounir Zrigui. "A Text Semantic Similarity Approach for Arabic Paraphrase Detection." In Computational Linguistics and Intelligent Text Processing, 338–49. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77116-8_25.
Повний текст джерелаKravchenko, Dmitry. "Paraphrase Detection Using Machine Translation and Textual Similarity Algorithms." In Communications in Computer and Information Science, 277–92. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71746-3_22.
Повний текст джерелаVíta, Martin. "Cross-lingual Metaphor Paraphrase Detection – Experimental Corpus and Baselines." In Communications in Computer and Information Science, 345–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59506-7_28.
Повний текст джерелаAnchiêta, Rafael Torres, and Thiago Alexandre Salgueiro Pardo. "Exploring the Potentiality of Semantic Features for Paraphrase Detection." In Lecture Notes in Computer Science, 228–38. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41505-1_22.
Повний текст джерелаSingh, Arwinder, and Gurpreet Singh Josan. "A Deep Network Model for Paraphrase Detection in Punjabi." In Lecture Notes in Electrical Engineering, 173–85. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8297-4_15.
Повний текст джерелаТези доповідей конференцій з теми "Paraphrase Detection"
Chi, Xiaoqiang, Yang Xiang, and Ruchao Shen. "Paraphrase Detection with Dependency Embedding." In CSAI 2020: 2020 4th International Conference on Computer Science and Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3445815.3445850.
Повний текст джерелаCordeiro, Joao, Gael Dias, and Pavel Brazdil. "A Metric for Paraphrase Detection." In 2007 International Multi-Conference on Computing in the Global Information Technology (ICCGI'07). IEEE, 2007. http://dx.doi.org/10.1109/iccgi.2007.4.
Повний текст джерелаUribe, Diego. "Monotonicity Analysis for Paraphrase Detection." In 2009 Electronics, Robotics and Automotive Mechanics Conference. IEEE, 2009. http://dx.doi.org/10.1109/cerma.2009.29.
Повний текст джерелаAn, Bo. "Chinese Paraphrase Dataset and Detection." In 2021 International Conference on Asian Language Processing (IALP). IEEE, 2021. http://dx.doi.org/10.1109/ialp54817.2021.9675232.
Повний текст джерелаBhargava, Rupal, Gargi Sharma, and Yashvardhan Sharma. "Deep Paraphrase Detection in Indian Languages." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3122119.
Повний текст джерелаIssa, Fuad, Marco Damonte, Shay B. Cohen, Xiaohui Yan, and Yi Chang. "Abstract Meaning Representation for Paraphrase Detection." In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/n18-1041.
Повний текст джерелаDuong, Phuc H., Hien T. Nguyen, Hieu N. Duong, Khoa Ngo, and Dat Ngo. "A Hybrid Approach to Paraphrase Detection." In 2018 5th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2018. http://dx.doi.org/10.1109/nics.2018.8606845.
Повний текст джерелаRohith, Mathi, Mothukuri Jaswanth Venkat, Pasumarthy Venkata Akhil, Mandiga Sahasra Sai Tarun, and Deepa Gupta. "Telugu Paraphrase Detection Using Siamese Network." In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2022. http://dx.doi.org/10.1109/icccnt54827.2022.9984593.
Повний текст джерелаWu, Wei, Yun-Cheng Ju, Xiao Li, and Ye-Yi Wang. "Paraphrase detection on SMS messages in automobiles." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5494959.
Повний текст джерелаMalajyan, Arthur, Karen Avetisyan, and Tsolak Ghukasyan. "ARPA: Armenian Paraphrase Detection Corpus and Models." In 2020 Ivannikov Memorial Workshop (IVMEM). IEEE, 2020. http://dx.doi.org/10.1109/ivmem51402.2020.00012.
Повний текст джерела