6533b85bfe1ef96bd12bab27
RESEARCH PRODUCT
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection
Giovanni PilatoGiosuè Lo BoscoMattia Antonino Di Gangisubject
FOS: Computer and information sciencesLinguistics and LanguageComputer Science - Machine LearningComputer sciencemedia_common.quotation_subjectSemantic spaceMachine Learning (stat.ML)02 engineering and technologycomputer.software_genreLanguage and LinguisticsTask (project management)Data-drivenMachine Learning (cs.LG)Artificial IntelligenceStatistics - Machine Learning020204 information systemsEveryday language0202 electrical engineering electronic engineering information engineeringSocial medianatural language processingmedia_commonComputer Science - Computation and LanguageSarcasmSettore INF/01 - Informaticabusiness.industryirony detectionIronymachine learningsemantic spaces020201 artificial intelligence & image processingArtificial intelligencebusinessIrony detectionsemantic spacecomputerComputation and Language (cs.CL)SoftwareNatural language processingsarcasm detectiondescription
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.
year | journal | country | edition | language |
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2019-01-01 |