A Robust Deep Ensemble Classifier for Figurative Language Detection

• URL: http://arxiv.org/abs/2107.04372v1
• Date: Fri, 9 Jul 2021 11:26:37 GMT
• Title: A Robust Deep Ensemble Classifier for Figurative Language Detection
• Authors: Rolandos Alexandros Potamias and Georgios Siolas and Andreas - Georgios Stafylopatis
• Abstract summary: Figurative Language (FL) recognition is an open problem of Sentiment Analysis in the broader field of Natural Language Processing (NLP) The problem itself contains three interrelated FL recognition tasks: sarcasm, irony and metaphor which, in the present paper, are dealt with advanced Deep Learning (DL) techniques. The Deep Soft Ensemble (DESC) model achieves a very good performance, worthy of comparison with relevant methodologies and state-of-the-art technologies in the challenging field of FL recognition.
• Score: 1.3124513975412255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recognition and classification of Figurative Language (FL) is an open problem of Sentiment Analysis in the broader field of Natural Language Processing (NLP) due to the contradictory meaning contained in phrases with metaphorical content. The problem itself contains three interrelated FL recognition tasks: sarcasm, irony and metaphor which, in the present paper, are dealt with advanced Deep Learning (DL) techniques. First, we introduce a data prepossessing framework towards efficient data representation formats so that to optimize the respective inputs to the DL models. In addition, special features are extracted in order to characterize the syntactic, expressive, emotional and temper content reflected in the respective social media text references. These features aim to capture aspects of the social network user's writing method. Finally, features are fed to a robust, Deep Ensemble Soft Classifier (DESC) which is based on the combination of different DL techniques. Using three different benchmark datasets (one of them containing various FL forms) we conclude that the DESC model achieves a very good performance, worthy of comparison with relevant methodologies and state-of-the-art technologies in the challenging field of FL recognition.

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