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1
XLM-T: A Multilingual Language Model Toolkit for Twitter ...
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2
Learning Cross-Lingual Word Embeddings from Twitter via Distant Supervision
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 14 (2020): Fourteenth International AAAI Conference on Web and Social Media; 72-82 ; 2334-0770 ; 2162-3449 (2020)
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3
Learning cross-lingual word embeddings from Twitter via distant supervision
Camacho Collados, Jose; Doval, Yerai; Martínez-Cámara, Eugenio. - : Association for the Advancement of Artificial Intelligence, 2020
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4
SemEval 2018 Task 2: Multilingual Emoji Prediction
Barbieri, Francesco; Camacho-Collados, Jose; Ronzano, Francesco. - : The Association for Computational Linguistics, 2018
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5
How Gender and Skin Tone Modifiers Affect Emoji Semantics in Twitter
Barbieri, Francesco; Camacho-Collados, Jose. - : The Association for Computational Linguistics, 2018
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6
Overview of the Evalita 2018 Italian Emoji Prediction (ITAmoji) Task
Ronzano, Francesco; Barbieri, Francesco; Wahyu Pamungkas, Endang. - : CEUR-WS, 2018. : country:DEU, 2018. : place:Aachen, 2018
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7
SemEval 2018 Task 2: Multilingual Emoji Prediction
Barbieri, Francesco; Camacho-Collados, Jose; Ronzano, Francesco. - : Association for Computational Linguistics, 2018. : country:USA, 2018. : place:Stroudsburg, PA, 2018
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8
Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges
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9
Overview of the Evalita 2016 SENTIment POLarity Classification Task
In: Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016) ; https://hal.inria.fr/hal-01414731 ; Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016), Dec 2016, Naples, Italy (2016)
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10
Overview of the Evalita 2016 Sentiment Polarity Classification Task
Barbieri, Francesco; Basile, Valerio; Croce, Danilo. - : CEUR-WS, 2016. : country:DEU, 2016. : place:Aachen, 2016
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11
Is This Tweet Satirical? A Computational Approach for Satire Detection in Spanish ; ¿Es satírico este tweet? Un método automático para la identificación del lenguaje satírico en español
Barbieri, Francesco; Ronzano, Francesco; Saggion, Horacio. - : Sociedad Española para el Procesamiento del Lenguaje Natural, 2015
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12
How gender and skin tone modifiers affect emoji semantics in Twitter
Barbieri, Francesco; Camacho-Collados, Jose. - : ACL (Association for Computational Linguistics)
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13
How gender and skin tone modifiers affect emoji semantics in Twitter
Barbieri, Francesco; Camacho-Collados, Jose. - : ACL (Association for Computational Linguistics)
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14
SemEval 2018 Task 2: multilingual emoji prediction
Barbieri, Francesco; Camacho-Collados, Jose; Ronzano, Francesco. - : ACL (Association for Computational Linguistics)
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15
Are emojis predictable?
Ballesteros, Miguel; Barbieri, Francesco; Saggion, Horacio. - : ACL (Association for Computational Linguistics)
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16
Interpretable emoji prediction via label-wise attention LSTMs
Espinosa-Anke, Luis; Camacho-Collados, Jose; Saggion, Horacio; Barbieri, Francesco; Schockaert, Steven. - : ACL (Association for Computational Linguistics)
Abstract: Comunicació presentada a la Conference on Empirical Methods in Natural Language Processing, celebrada del 31 d'octubre al 4 de novembre de 2018 a Brussel·les, Bèlgica. ; Human language has evolved towards newer forms of communication such as social media, where emojis (i.e., ideograms bearing a visual meaning) play a key role. While there is an increasing body of work aimed at the computational modeling of emoji semantics, there is currently little understanding about what makes a computational model represent or predict a given emoji in a certain way. In this paper we propose a label-wise attention mechanism with which we attempt to better understand the nuances underlying emoji prediction. In addition to advantages in terms of interpretability, we show that our proposed architecture improves over standard baselines in emoji prediction, and does particularly well when predicting infrequent emojis. ; F. Barbieri and H. Saggion acknowledge support from the TUNER project (TIN2015-65308-C5-5- R, MINECO/FEDER, UE). Luis Espinosa-Anke, Jose Camacho-Collados and Steven Schockaert have been supported by ERC Starting Grant 637277.
URL: http://hdl.handle.net/10230/35940
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17
Multimodal emoji prediction
Ronzano, Francesco; Barbieri, Francesco; Saggion, Horacio. - : ACL (Association for Computational Linguistics)
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18
Towards the understanding of gaming audiences by modeling Twitch emotes
Ballesteros, Miguel; Barbieri, Francesco; Saggion, Horacio. - : ACL (Association for Computational Linguistics)
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