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Deep Fusion of Multiple Term-Similarity Measures For Biomedical Passage Retrieval
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22 |
A Twitter Political Corpus of the 2019 10N Spanish Election
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Do Linguistic Features Help Deep Learning? The Case of Aggressiveness in Mexican Tweets
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Multimodal Fake News Detection with Textual, Visual and Semantic Information
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An Emotional Analysis of False Information in Social Media and News Articles
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26 |
Irony Detection in Twitter with Imbalanced Class Distributions
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27 |
#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
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Fine-Grained Analysis of Language Varieties and Demographics
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Multilingual Stance Detection in Social Media Political Debates
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Fake Opinion Detection: How Similar are Crowdsourced Datasets to Real Data?
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33 |
Overview of PAN 2020: Authorship Verification, Celebrity Profiling, Profiling Fake News Spreaders on Twitter, and Style Change Detection
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The Role of Personality and Linguistic Patterns in Discriminating Between Fake News Spreaders and Fact Checkers
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Scalable and Language-Independent Embedding-based Approach for Plagiarism Detection Considering Obfuscation Type: No Training Phase
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Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions
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Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter
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IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
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Abstract:
[EN] This overview paper describes the first shared task on irony detection for the Arabic language. The task consists of a binary classification of tweets as ironic or not using a dataset composed of 5,030 Arabic tweets about different political issues and events related to the Middle East and the Maghreb. Tweets in our dataset are written in Modern Standard Arabic but also in different Arabic language varieties including Egypt, Gulf, Levantine and Maghrebi dialects. Eighteen teams registered to the task among which ten submitted their runs. The methods of participants ranged from feature-based to neural networks using either classical machine learning techniques or ensemble methods. The best performing system achieved F-score value of 0.844, showing that classical feature-based models outperform the neural ones. ; This publication was made possible by NPRP grant 9-175-1-033 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the last author. The work of Paolo Rosso was also partially funded by Generalitat Valenciana under grant PROMETEO/2019/121. ; Ghanem, B.; Karoui, J.; Benamara, F.; Moriceau, V.; Rosso, P. (2019). IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets. CEUR-WS.org. 380-390. http://hdl.handle.net/10251/180744 ; S ; 380 ; 390
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Keyword:
Arabic language; Irony detection; LENGUAJES Y SISTEMAS INFORMATICOS; Social media
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URL: http://hdl.handle.net/10251/180744
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