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1
Categorizing Misogynistic Behaviours in Italian, English and Spanish Tweets
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2
Multilingual Irony Detection with Dependency Syntax and Neural Models
Alessandra Teresa Cignarella, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, Farah Benamara and Paolo Rosso. - : Association for Computational Linguistics, 2020. : country:USA, 2020. : place:Stroudsburg, PA, 2020
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3
UPV-28-UNITO at SemEval-2019 Task 7: Exploiting Post’s Nesting and Syntax Information for Rumor Stance Classification
Bilal Ghanem, Alessandra Teresa Cignarella, Cristina Bosco, Paolo Rosso, Francisco Rangel. - : Association for Computational Linguistics (ACL), 2019. : country:USA, 2019. : place:Stroudsburg, PA, 2019
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4
SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana, Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, Manuela Sanguinetti. - : Association for Computational Linguistics, 2019. : country:USA, 2019. : place:Stroudsburg, 2019
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5
Presenting TWITTIRÒ-UD: An Italian Twitter Treebank in Universal Dependencies
Alessandra Teresa Cignarella, Cristina Bosco, Paolo Rosso. - : ACL, 2019. : country:USA, 2019. : place:Stroudsburg, PA, 2019
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6
Stance Evolution and Twitter Interactions in an Italian Political Debate
Mirko Lai, Viviana Patti, Giancarlo Ruffo, Paolo Rosso. - : Springer International Publishing AG, part of Springer Nature 2018, 2018. : country:CHE, 2018. : place:Cham (ZG), 2018
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7
Query expansion for mixed-script information retrieval.
In: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/SIGIR_2014_MSIR.pdf (2014)
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8
R.: Plagiarism meets paraphrasing: Insights for the next generation in automatic plagiarism detection
In: http://wing.comp.nus.edu.sg/~antho/J/J13/J13-4005.pdf (2013)
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9
The use of orthogonal similarity relations in the prediction of authorship
In: http://ccc.inaoep.mx/~mmontesg/publicaciones/2013/AuthorshipAttributionWithOrthogonalFeatures-Cicling13.pdf (2013)
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10
Author manuscript, published in "13th Ibero-American Conference on AI, Cartagena de Indias: Colombia (2012)" Voice-QA: Evaluating the Impact of Misrecognized Words on Passage Retrieval
In: http://hal.inria.fr/docs/00/82/52/46/PDF/IDF-VAPR-CIKM.pdf (2013)
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11
1st International Workshop on Emotion and Sentiment in Social and Expressive Media approaches and perspectives from AI Edited by
In: http://ceur-ws.org/Vol-1096/proceedings.pdf (2013)
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12
A multidimensional approach for detecting irony
In: http://afflatus.ucd.ie/Papers/reyesirony.pdf (2013)
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13
Plagiarism detection across distant language pairs
In: http://lexitron.nectec.or.th/public/COLING-2010_Beijing_China/PAPERS/pdf/PAPERS005.pdf (2010)
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14
Plagiarism detection across distant language pairs
In: http://ixa.si.ehu.es/Ixa/Argitalpenak/Artikuluak/1283944120/publikoak/coling-plagiarism/ (2010)
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15
Automatic Ontology Matching Via Upper Ontologies: A Systematic Evaluation
In: http://www.disi.unige.it/person/MascardiV/Download/TKDE-maggio-2009.pdf (2009)
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16
3rd PAN WORKSHOP. UNCOVERING PLAGIARISM, AUTHORSHIP AND SOCIAL SOFTWARE MISUSE 25th ANNUAL CONFERENCE OF THE SPANISH SOCIETY FOR NATURAL LANGUAGE PROCESSING, SEPLN 2009
In: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-502/pan09-proceedings.pdf (2009)
Abstract: The PAN workshop brought together experts and researchers around the exciting and future-oriented topics of plagiarism detection, authorship identification, and the detection of social software misuse. The development of new solutions for these problems can benefit from the combination of existing technologies, and in this sense the workshop provides a platform that spans different views and approaches. Plagiarism analysis is a collective term for computer-based methods to identify a plagiarism offense. In connection with text documents we distinguish between corpus-based and intrinsic analysis: the former compares suspicious documents against a set of potential original documents, the latter identifies potentially plagiarized passages by analyzing the suspicious document with respect to changes in writing style. Authorship identification divides into so-called attribution and verification problems. In the authorship attribution problem, one is given examples of the writing of a number of authors and is asked to determine which of them authored given anonymous texts. In the authorship verification problem, one is given examples of the writing of a single author and is asked to determine if given texts were or were not written by this author. As a categorization problem, verification is significantly more difficult than attribution. Authorship verification and intrinsic plagiarism analysis represent two sides of the same coin.
URL: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-502/pan09-proceedings.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.204.5886
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17
L.: A Web-Based Self-training Approach for Authorship Attribution
In: http://ccc.inaoep.mx/~villasen/articulos/SelftrainingAuthorshipAttribution-GoTal08.pdf (2008)
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18
Arabic named entity recognition using optimized feature sets
In: http://www.aclweb.org/anthology-new/D/D08/D08-1030.pdf (2008)
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19
Specific Features to Enhance Arabic Named Entity Recognition
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20
Geo-wordnet: Automatic georeferencing of wordnet
In: http://users.dsic.upv.es/~prosso/resources/BuscaldiRosso_LREC08.pdf (2008)
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