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Dependency Syntax in the Automatic Detection of Irony and Stance ; Sintaxis de dependencias en la detección automática de ironía y posicionamiento
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Enjeux liés à la détection de l’ironie
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In: Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 2 : 23e REncontres jeunes Chercheurs en Informatique pour le TAL (RECITAL) ; Traitement Automatique des Langues Naturelles ; https://hal.archives-ouvertes.fr/hal-03265905 ; Traitement Automatique des Langues Naturelles, 2021, Lille, France. pp.55-66 (2021)
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Dependency Syntax in the Automatic Detection of Irony and Stance
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Contributions to the Computational Treatment of Non-literal Language
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Irony Detection in Twitter with Imbalanced Class Distributions
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Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter
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RGCL at IDAT: deep learning models for irony detection in Arabic language
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In: 2517 ; 416 ; 425 (2019)
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Computational models for irony detection in three Spanish variants
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Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection
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Abstract:
Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine-learning algorithms to input texts sub-symbolically represented in a Latent Semantic space. The main consequence is that our studies establish both reference datasets and baselines for the sarcasm detection problem that could serve the scientific community to test newly proposed methods
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Keyword:
irony detection; machine learning; natural language processing; sarcasm detection; semantic spaces; Settore INF/01 - Informatica
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URL: http://hdl.handle.net/10447/349361 https://doi.org/10.1017/S1351324919000019
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IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
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WLV at SemEval-2018 task 3: Dissecting tweets in search of irony
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A survey on author profiling, deception, and irony detection for the Arabic language
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Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges
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A Knowledge-Based Weighted KNN for Detecting Irony in Twitter
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A survey on author profiling, deception, and irony detection for the Arabic language
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SOUKHRIA: Towards an Irony Detection System for Arabic in Social Media
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In: 3rd International Conference on Arabic Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01686504 ; 3rd International Conference on Arabic Computational Linguistics, Nov 2017, Dubaï, United Arab Emirates. pp.161 - 168, ⟨10.1016/j.procs.2017.10.105⟩ (2017)
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Irony and Sarcasm Detection in Twitter: The Role of Affective Content
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Special Session on Emotion and Sentiment in Intelligent Systems and Big Social Data Analysis (SentISData 2016)
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In: 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016) ; https://hal.archives-ouvertes.fr/hal-03176429 ; Benamara, Farah; Bosco, Cristina; Fersini, Elisabetta; Patti, Viviana; Viviancos, Emilio. 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016), Oct 2016, Montréal, Canada. 2016 ; https://sites.ualberta.ca/~dsaa16/specialsessions.html (2016)
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Character and Word Baselines for Irony Detection in Spanish Short Texts ; Sistemas de detección de ironía basados en palabras y caracteres para textos cortos en español
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