Psycho-linguistic Experiment on Universal Semantic Components of Verbal Humor: System Description and Annotation
- URL: http://arxiv.org/abs/2407.07617v1
- Date: Wed, 10 Jul 2024 12:56:17 GMT
- Title: Psycho-linguistic Experiment on Universal Semantic Components of Verbal Humor: System Description and Annotation
- Authors: Elena Mikhalkova, Nadezhda Ganzherli, Julia Murzina,
- Abstract summary: We give an in-depth observation of our system of self-paced reading for annotation of humor.
The system registers keys that readers press to open the next word, choose a class (humorous versus non-humorous texts), change their choice.
We also touch upon our psycho-linguistic experiment conducted with the system and the data collected during it.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Objective criteria for universal semantic components that distinguish a humorous utterance from a non-humorous one are presently under debate. In this article, we give an in-depth observation of our system of self-paced reading for annotation of humor, that collects readers' annotations while they open a text word by word. The system registers keys that readers press to open the next word, choose a class (humorous versus non-humorous texts), change their choice. We also touch upon our psycho-linguistic experiment conducted with the system and the data collected during it.
Related papers
- LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification [0.0]
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.
SHAP interpretations and decision trees identify influential features, revealing that a holistic approach improves humor detection accuracy on unseen data.
arXiv Detail & Related papers (2024-08-12T17:52:11Z) - The Naughtyformer: A Transformer Understands Offensive Humor [63.05016513788047]
We introduce a novel jokes dataset filtered from Reddit and solve the subtype classification task using a finetuned Transformer dubbed the Naughtyformer.
We show that our model is significantly better at detecting offensiveness in jokes compared to state-of-the-art methods.
arXiv Detail & Related papers (2022-11-25T20:37:58Z) - This joke is [MASK]: Recognizing Humor and Offense with Prompting [9.745213455946324]
Humor is a magnetic component in everyday human interactions and communications.
We investigate the effectiveness of prompting, a new transfer learning paradigm for NLP, for humor recognition.
arXiv Detail & Related papers (2022-10-25T13:02:45Z) - ExPUNations: Augmenting Puns with Keywords and Explanations [88.58174386894913]
We augment an existing dataset of puns with detailed crowdsourced annotations of keywords.
This is the first humor dataset with such extensive and fine-grained annotations specifically for puns.
We propose two tasks: explanation generation to aid with pun classification and keyword-conditioned pun generation.
arXiv Detail & Related papers (2022-10-24T18:12:02Z) - Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results [84.37263300062597]
Humor is a substantial element of human social behavior, affect, and cognition.
Current methods of humor detection have been exclusively based on staged data, making them inadequate for "real-world" applications.
We contribute to addressing this deficiency by introducing the novel Passau-Spontaneous Football Coach Humor dataset, comprising about 11 hours of recordings.
arXiv Detail & Related papers (2022-09-28T17:36:47Z) - Seeking Subjectivity in Visual Emotion Distribution Learning [93.96205258496697]
Visual Emotion Analysis (VEA) aims to predict people's emotions towards different visual stimuli.
Existing methods often predict visual emotion distribution in a unified network, neglecting the inherent subjectivity in its crowd voting process.
We propose a novel textitSubjectivity Appraise-and-Match Network (SAMNet) to investigate the subjectivity in visual emotion distribution.
arXiv Detail & Related papers (2022-07-25T02:20:03Z) - DeHumor: Visual Analytics for Decomposing Humor [36.300283476950796]
We develop DeHumor, a visual system for analyzing humorous behaviors in public speaking.
To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features.
We show that DeHumor is able to highlight various building blocks of humor examples.
arXiv Detail & Related papers (2021-07-18T04:01:07Z) - Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting
Incongruity-Based Features for Humor Recognition [0.6445605125467573]
We break down any joke into two distinct components: the set-up and the punchline.
Inspired by the incongruity theory of humor, we model the set-up as the part developing semantic uncertainty.
With increasingly powerful language models, we were able to feed the set-up along with the punchline into the GPT-2 language model.
arXiv Detail & Related papers (2020-12-22T13:48:09Z) - Predicting the Humorousness of Tweets Using Gaussian Process Preference
Learning [56.18809963342249]
We present a probabilistic approach that learns to rank and rate the humorousness of short texts by exploiting human preference judgments and automatically sourced linguistic annotations.
We report system performance for the campaign's two subtasks, humour detection and funniness score prediction, and discuss some issues arising from the conversion between the numeric scores used in the HAHA@IberLEF 2019 data and the pairwise judgment annotations required for our method.
arXiv Detail & Related papers (2020-08-03T13:05:42Z) - "The Boating Store Had Its Best Sail Ever": Pronunciation-attentive
Contextualized Pun Recognition [80.59427655743092]
We propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor.
PCPR derives contextualized representation for each word in a sentence by capturing the association between the surrounding context and its corresponding phonetic symbols.
Results demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in pun detection and location tasks.
arXiv Detail & Related papers (2020-04-29T20:12:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.