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, 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Feature EngineeringHumor DetectionSentenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
LOLGORITHM: Funny Comment Generation Agent For Short Videos
Short-form video platforms have become central to multimedia information dissemination, where comments play a critical role in driving engagement, propagation, and algorithmic feedback. However, existing approaches -- in…
Video ClassificationVideo SummarizationSemantic RetrievalLaugh, Relate, Engage: Stylized Comment Generation for Short Videos
Short-video platforms have become a central medium in the modern Internet landscape, where efficient information delivery and strong interactivity are reshaping user engagement and cultural dissemination. Among the vario…
Video SegmentationA Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents
Multiturn dialogue models aim to generate human-like responses by leveraging conversational context, consisting of utterances from previous exchanges. Existing methods often neglect the interactions between these utteran…
Sense Contextualization in a Dependency-Based Compositional Distributional Model
Little attention has been paid to distributional compositional methods which employ syntactically structured vector models. As word vectors belonging to different syntactic categories have incompatible syntactic distribu…
LEMMARepresentation LearningSemantic CompositionWord Sense DisambiguationIntegrating Language-Derived Appearance Elements with Visual Cues in Pedestrian Detection
Large language models (LLMs) have shown their capabilities in understanding contextual and semantic information regarding knowledge of instance appearances. In this paper, we introduce a novel approach to utilize the str…
Pedestrian Detection