Year: 2018
Type: conference-paper
Abstract: This paper describes our system, entitled IronyMagnet, for the 3rd Task of the SemEval 2018 workshop, "Irony Detection in English Tweets".In Task 1, irony classification task has been considered as a binary classification task.Now for the first time, finer categories of irony are considered as part of a shared task.In task 2, three types of irony are considered; "Irony by contrast" -ironic instances where evaluative expression portrays inverse polarity (positive, negative) of the literal proposition; "Situational irony" -ironic instances where output of a situation do not comply with its expectation; "Other verbal irony" -instances where ironic intent does not rely on polarity contrast or unexpected outcome.We proposed a Siamese neural network for irony detection, which is consisted of two subnetworks, each containing a long short term memory layer (LSTM) and an embedding layer initialized with vectors from Glove word embedding 1 .The system achieved a f-score of 0.72, and 0.50 in task 1, and task 2 respectively.
DOI: 10.18653/v1/s18-1093
Authors: Aniruddha Ghosh, Tony Veale
Institution: University College Dublin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2805862862