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

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, W...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Khurana, Tanisha, Kaushik Pillalamarri, Pande, Vikram, Singh, Munindar
Format: Article
Language:English
Subjects:
Decision trees
Linguistics
Natural language processing
Semantics
Online Access:Get full text
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Summary: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.
ISSN:2331-8422