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Applying basic features from sentiment analysis on automatic irony detection
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Cross-language source code re-use detection using latent semantic analysis
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Language variety identification using distributed representations of words and documents
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Cross-domain polarity classification using a knowledge-enhanced meta-classifier
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On the multilingual and genre robustness of EmoGraphs for author profiling in social media
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Overview of the Evalita 2014 SENTIment POLarity Classification Task
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In: Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14) ; https://hal.inria.fr/hal-01228925 ; Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14), 2014, Pisa, Italy. ⟨10.12871/clicit201429⟩ (2014)
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Overview of the Evalita 2014 SENTIment POLarity Classification Task
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In: Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14) ; https://hal.inria.fr/hal-01195786 ; Proceedings of the 4th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA'14), Dec 2014, Pisa, France. ⟨10.12871/clicit201429⟩ (2014)
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Construction of an ontology for intelligent Arabic QA systems leveraging the Conceptual Graphs representation
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On the difficulty of automatically detecting irony: beyond a simple case of negation
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Abstract:
The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-013-0652-8 ; It is well known that irony is one of the most subtle devices used to, in a refined way and without a negation marker, deny what is literally said. As such, its automatic detection would represent valuable knowledge regarding tasks as diverse as sentiment analysis, information extraction, or decision making. The research described in this article is focused on identifying key values of components to represent underlying characteristics of this linguistic phenomenon. In the absence of a negation marker, we focus on representing the core of irony by means of three conceptual layers. These layers involve 8 different textual features. By representing four available data sets with these features, we try to find hints about how to deal with this unexplored task from a computational point of view. Our findings are assessed by human annotators in two strata: isolated sentences and entire documents. The results show how complex and subjective the task of automatically detecting irony could be. ; The research work of Paolo Rosso was done in the framework of the European Commission WIQ-EI Web Information Quality Evaluation Initiative (IRSES grant no. 269180) project within the FP 7 Marie Curie People, the DIANA-APPLICATIONS - Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. ; Reyes Pérez, A.; Rosso, P. (2014). On the difficulty of automatically detecting irony: beyond a simple case of negation. Knowledge and Information Systems. 40(3):595-614. https://doi.org/10.1007/s10115-013-0652-8 ; S ; 595 ; 614 ; 40 ; 3 ; Artstein R, Poesio M (2008) Inter-coder agreement for computational linguistics. 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In: ACL-IJCNLP ’09: proceedings of the ACL-IJCNLP 2009 conference short papers, pp 161–164 ; Carvalho P, Sarmento L, Silva M, de Oliveira E (2009) Clues for detecting irony in user-generated contents: oh.!! It’s “so easy”; -). In: TSA ’09: proceeding of the 1st international CIKM workshop on topic-sentiment analysis for mass opinion. ACM, Hong Kong, China, pp 53–56 ; Clark H, Gerrig R (1984) On the pretense theory of irony. J Exp Psychol Gen 113(1):121–126 ; Colston H (2007) On necessary conditions for verbal irony comprehension. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London, pp 97–134 ; Colston H, Gibbs R (2007) A brief history of irony. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London, pp 3–24 ; Curcó C (2007) Irony: negation, echo, and metarepresentation. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London, pp 269–296 ; Davidov D, Tsur O, Rappoport A (2010) Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In: Proceedings of the 14th conference on computational natural language learning, CoNLL ’10. Association for Computational Linguistics, Stroudsburg, PA, USA, pp 107–116 ; Francisco V, Gervás P, Peinado F (2010) Ontological reasoning for improving the treatment of emotions in text. Knowl Inf Syst 24(2):23 ; Gibbs R (2007) Irony in talk among friends. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London, pp 339–360 ; Gibbs R, Colston H (2007) The future of irony studies. In: Gibbs R, Colston H (eds) Irony in language and thought. Taylor and Francis Group, London ; Giora R (1995) On irony and negation. Discourse Process 19(2):239–264 ; Giora R, Balaban N, Fein O, Alkabets I (2005) Negation as positivity in disguise. 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Taylor and Francis Group, London, pp 35–56 ; Zagibalov T, Belyatskaya K, Carroll J (2010) Comparable English-Russian book review corpora for sentiment analysis. In: Proceedings of the 1st workshop on computational approaches to subjectivity and sentiment analysis. Lisbon, Portugal, pp 67–72
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Keyword:
Figurative language processing; Irony detection; LENGUAJES Y SISTEMAS INFORMATICOS; Negation
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URL: https://doi.org/10.1007/s10115-013-0652-8
http://hdl.handle.net/10251/40330
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| 98 |
Detección de plagio translingüe utilizando una red semántica multilingüe
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A Comparison of Approaches for Measuring Cross-Lingual Similarity of Wikipedia Articles
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On the Design of an Intelligent Sensor Network for Flash Flood Monitoring, Diagnosis and Management in Urban Areas
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