PunFields at SemEval-2017 Task 7: Employing Roget's Thesaurus in Automatic Pun Recognition and Interpretation
This work addresses the problem of computational humor understanding for NLP researchers, but it is incremental as it builds on existing thesaurus-based methods and requires improvement in target word detection.
The authors tackled automatic pun interpretation by developing PunFields, a model using Roget's Thesaurus to identify semantic fields and classify puns with an SVM, achieving considerably good results in pun classification at SemEval Task 7.
The article describes a model of automatic interpretation of English puns, based on Roget's Thesaurus, and its implementation, PunFields. In a pun, the algorithm discovers two groups of words that belong to two main semantic fields. The fields become a semantic vector based on which an SVM classifier learns to recognize puns. A rule-based model is then applied for recognition of intentionally ambiguous (target) words and their definitions. In SemEval Task 7 PunFields shows a considerably good result in pun classification, but requires improvement in searching for the target word and its definition.