Sarcasm target identification: Dataset and an introductory approach

Abstract

Past work in computational sarcasm deals primarily with sarcasm detection. In this paper, we introduce a novel, related problem: sarcasm target identification (i.e., extracting the target of ridicule in a sarcastic sentence). As a benchmark, we introduce a new dataset for the task. This dataset is manually annotated for the sarcasm target in book snippets and tweets based on our formulation of the task. We then introduce an automatic approach for sarcasm target identification. It is based on a combination of two types of extractors: one based on rules, and another consisting of a statistical classifier. Our introductory approach establishes the viability of sarcasm target identification, and will serve as a baseline for future work. © LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved.