LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification

TL;DR

This paper presents a humor detection model that combines syntactic, semantic, and contextual linguistic features, utilizing BERT embeddings and interpretability tools to improve accuracy in understanding humor.

Contribution

It introduces a novel holistic approach integrating multiple linguistic features with BERT for humor classification, emphasizing interpretability and feature importance.

Findings

Holistic feature integration improves humor detection accuracy.

BERT embeddings combined with linguistic features outperform baseline models.

SHAP analysis identifies key features influencing humor classification.

Abstract

This paper explores humor detection through a linguistic lens, prioritizing syntactic, semantic, and contextual features over computational methods in Natural Language Processing. We categorize features into syntactic, semantic, and contextual dimensions, including lexicons, structural statistics, Word2Vec, WordNet, and phonetic style. Our proposed model, Colbert, utilizes BERT embeddings and parallel hidden layers to capture sentence congruity. By combining syntactic, semantic, and contextual features, we train Colbert for humor detection. Feature engineering examines essential syntactic and semantic features alongside BERT embeddings. SHAP interpretations and decision trees identify influential features, revealing that a holistic approach improves humor detection accuracy on unseen data. Integrating linguistic cues from different dimensions enhances the model's ability to understand humor complexity beyond traditional computational methods.