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EVALITA Evaluation of NLP and Speech Tools for Italian

Proceedings of the Final Workshop 12-13 December 2018, Naples

EVALITA is a periodic evaluation campaign of Natural Language Processing (NLP) and speech tools for the Italian language.

The general objective of EVALITA is to promote the development of language and speech technologies for the Italian language, providing a shared framework where different systems and approaches can be evaluated in a consistent manner.

The diffusion of shared tasks and shared evaluation practices is a crucial step towards the development of resources and tools for...


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  • Verlag : Accademia University Press
  • Buchreihe : Collana dell'Associazione Italiana di Linguistica Computazionale
  • Erscheinungsort : Torino
  • Erscheinungsjahr : 2018
  • Auf OpenEdition Books veröffentlicht : 05 juin 2019
  • EAN (Printversion) : 978-88-31978-42-2
  • EAN (elektronische Version) : 978-88-31978-69-9
  • DOI : 10.4000/books.aaccademia.4421
  • Seitenzahl : 281 p.
Tommaso Caselli, Nicole Novielli, Viviana Patti et al.
Preface to the Evalita 2018 Proceedings

Part I. Introduction to Evalita 2018 and task overviews

Pierpaolo Basile, Danilo Croce, Valerio Basile et al.
Overview of the EVALITA 2018 Aspect-based Sentiment Analysis task (ABSITA)
Francesco Ronzano, Francesco Barbieri, Endang Wahyu Pamungkas et al.
Overview of the EVALITA 2018 Italian Emoji Prediction (ITAMoji) Task
Alessandra Teresa Cignarella, Simona Frenda, Valerio Basile et al.
Overview of the EVALITA 2018 Task on Irony Detection in Italian Tweets (IronITA)
Francesco Cutugno, Maria Di Maro, Sara Falcone et al.
Overview of the EVALITA 2018 Evaluation of Italian DIALogue systems (IDIAL) Task
Elisabetta Fersini, Debora Nozza und Paolo Rosso
Overview of the Evalita 2018 Task on Automatic Misogyny Identification (AMI)
Cristina Bosco, Felice Dell’Orletta, Fabio Poletto et al.
Overview of the EVALITA 2018 Hate Speech Detection Task
Pierpaolo Basile, Marco de Gemmis, Lucia Siciliani et al.
Overview of the EVALITA 2018 Solving language games (NLP4FUN) Task

Part II. Participant reports

Andrea Cimino, Lorenzo De Mattei und Felice Dell’Orletta
Multi-task Learning in Deep Neural Networks at EVALITA 2018
Emanuele Di Rosa und Alberto Durante
Aspect-based Sentiment Analysis: X2Check at ABSITA 2018
Daniele Di Sarli, Claudio Gallicchio und Alessio Micheli
ITAmoji 2018: Emoji Prediction via Tree Echo State Networks
Andrei Catalin Coman, Yaroslav Nechaev und Giacomo Zara
Predicting Emoji Exploiting Multimodal Data: FBK Participation in ITAmoji Task
Andrea Santilli, Danilo Croce und Roberto Basili
A Kernel-based Approach for Irony and Sarcasm Detection in Italian
Emanuele Di Rosa und Alberto Durante
Irony detection in tweets: X2Check at Ironita 2018
Reynier Ortega-Bueno und José E. Medina Pagola
UO_IRO: Linguistic informed deep-learning model for irony detection
Simona Frenda, Bilal Ghanem, Estefanía Guzmán-Falcón et al.
Automatic Expansion of Lexicons for Multilingual Misogyny Detection
Resham Ahluwalia, Himani Soni, Edward Callow et al.
Detecting Hate Speech Against Women in English Tweets
Endang Wahyu Pamungkas, Alessandra Teresa Cignarella, Valerio Basile et al.
Automatic Identification of Misogyny in English and Italian Tweets at EVALITA 2018 with a Multilingual Hate Lexicon
Paula Fortuna, Ilaria Bonavita und Sérgio Nunes
Merging datasets for hate speech classification in Italian
Michele Corazza, Stefano Menini, Pinar Arslan et al.
Comparing Different Supervised Approaches to Hate Speech Detection
Gretel Liz De la Peña Sarracén, Reynaldo Gil Pons, Carlos Enrique Muñiz Cuza et al.
Hate Speech Detection using Attention-based LSTM
Valentino Santucci, Stefania Spina, Alfredo Milani et al.
Detecting Hate Speech for Italian Language in Social Media
Xiaoyu Bai, Flavio Merenda, Claudia Zaghi et al.
RuG @ EVALITA 2018: Hate Speech Detection In Italian Social Media
Federico Sangati, Antonio Pascucci und Johanna Monti
Exploiting Multiword Expressions to solve “La Ghigliottina”
Simone Magnolini, Vevake Balaraman, Marco Guerini et al.
The Perfect Recipe: Add SUGAR, Add Data

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