Summary of Lolgorithm: Integrating Semantic,syntactic and Contextual Elements For Humor Classification, by Tanisha Khurana et al.
LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification
by Tanisha Khurana, Kaushik Pillalamarri, Vikram Pande, Munindar Singh
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to detecting humor in text is proposed by this paper, focusing on linguistic features such as syntax, semantics, and context rather than relying solely on Natural Language Processing (NLP) methods. The study categorizes these features into three dimensions: syntactic, semantic, and contextual, including lexical statistics, Word2Vec, WordNet, and phonetic style. A new model called Colbert is introduced, which utilizes BERT embeddings and parallel hidden layers to capture sentence congruity. By combining these features, the authors train Colbert for humor detection, with feature engineering revealing essential syntactic and semantic features alongside BERT embeddings. SHAP interpretations and decision trees identify influential features, demonstrating that a holistic approach improves accuracy on unseen data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to tell when something is funny by using words and language patterns. The researchers group these patterns into three types: word order, meaning, and context. They create a new model called Colbert that uses special language processing tools to understand what makes something funny. By combining all these features, the model gets better at detecting humor in texts it hasn’t seen before. |
Keywords
* Artificial intelligence * Bert * Feature engineering * Natural language processing * Nlp * Semantics * Syntax * Word2vec
