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

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Khurana, Tanisha, Pillalamarri, Kaushik, Pande, Vikram, Singh, Munindar
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866910563802546176
author Khurana, Tanisha
Pillalamarri, Kaushik
Pande, Vikram
Singh, Munindar
author_facet Khurana, Tanisha
Pillalamarri, Kaushik
Pande, Vikram
Singh, Munindar
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2408_06335
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification
Khurana, Tanisha
Pillalamarri, Kaushik
Pande, Vikram
Singh, Munindar
Computation and Language
Artificial Intelligence
Machine Learning
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.
title LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification
topic Computation and Language
Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2408.06335