Detecting Intentional Lexical Ambiguity in English Puns
This work addresses the challenge of identifying puns in natural language processing, which is important for applications like humor analysis or text understanding, but it is incremental as it builds on existing competition tasks and methods.
The authors tackled the problem of automatically detecting intentional lexical ambiguity in English puns by using Roget's Thesaurus to create semantic vectors and an SVM classifier, achieving an F-measure score of 0.73. They also applied rule-based methods to locate ambiguous words, finding structural criteria more effective but potentially dataset-specific.
The article describes a model of automatic analysis of puns, where a word is intentionally used in two meanings at the same time (the target word). We employ Roget's Thesaurus to discover two groups of words which, in a pun, form around two abstract bits of meaning (semes). They become a semantic vector, based on which an SVM classifier learns to recognize puns, reaching a score 0.73 for F-measure. We apply several rule-based methods to locate intentionally ambiguous (target) words, based on structural and semantic criteria. It appears that the structural criterion is more effective, although it possibly characterizes only the tested dataset. The results we get correlate with the results of other teams at SemEval-2017 competition (Task 7 Detection and Interpretation of English Puns) considering effects of using supervised learning models and word statistics.