Joint Detection and Location of English Puns
This addresses the challenge of identifying puns in text for natural language processing applications, but it is incremental as it builds on existing methods for pun detection.
The paper tackled the problem of detecting and locating English puns by proposing a joint sequence labeling approach with a new tagging scheme, achieving new state-of-the-art results on benchmark datasets.
A pun is a form of wordplay for an intended humorous or rhetorical effect, where a word suggests two or more meanings by exploiting polysemy (homographic pun) or phonological similarity to another word (heterographic pun). This paper presents an approach that addresses pun detection and pun location jointly from a sequence labeling perspective. We employ a new tagging scheme such that the model is capable of performing such a joint task, where useful structural information can be properly captured. We show that our proposed model is effective in handling both homographic and heterographic puns. Empirical results on the benchmark datasets demonstrate that our approach can achieve new state-of-the-art results.