Papers by Carlo Strapparava
A Computational Exploration of Exaggeration (D18-1)
Copied to clipboard
| Challenge: | a new computational approach to exaggeration detection is needed for non-literal phenomena . a corpus of overstatements (or hyperboles) is used to detect exaggrements . |
| Approach: | They propose a computational approach to detect exaggerated sentences using crowdsourcing data . they build a corpus containing overstatements and then evaluate models trained on HYPO . |
| Outcome: | The proposed approach can detect exaggerated sentences using a crowdsourced dataset. |
Punctuation as Native Language Interference (C18-1)
Copied to clipboard
| Challenge: | Numerous aspects of written language have been studied for native language identification (NLI) but its impact has not been studied. |
| Approach: | They propose to use punctuation marks as indicators of native language . they propose to apply them to native language identification . |
| Outcome: | The proposed methods support the hypothesis that punctuation marks are persistent and robust indicators of the native language of the author, even when a high proficiency level in a non-native language is achieved. |
Making People Laugh like a Pro: Analysing Humor Through Stand-Up Comedy (2022.lrec-1)
Copied to clipboard
| Challenge: | a lot of computational tools focus on standalone jokes or on occasional humorous sentences during presentations. |
| Approach: | They propose to use stand-up comedy transcripts to extract humor from a larger narrative. |
| Outcome: | The dataset, SCRIPTS, is built using stand-up comedy shows transcripts. |
EmoEvent: A Multilingual Emotion Corpus based on different Events (2020.lrec-1)
Copied to clipboard
| Challenge: | In recent years, emotion detection in text has become more popular due to its potential applications in fields such as psychology, marketing, political science, among others. |
| Approach: | They propose to use an annotated dataset to identify emotions in tweets from different events that took place in April 2019 to validate the effectiveness of the data set. |
| Outcome: | The proposed method is based on a multilingual emotion data set based in different events that took place in April 2019 in English and Spanish. |
DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text (2020.lrec-1)
Copied to clipboard
| Challenge: | Recent studies show that humans are ineffective in spotting deceit, with accuracy rates only slightly above the chance level. |
| Approach: | They propose a new language resource for automatic deception detection in cross-domain and cross-language scenarios. |
| Outcome: | The proposed language resource is composed of 5000 examples of truthful and deceitful first-person opinions across five different domains and two languages. |
VROAV: Using Iconicity to Visually Represent Abstract Verbs (2020.lrec-1)
Copied to clipboard
| Challenge: | Visual languages like sign languages reveal enlightening patterns across signs of similar meanings, pointing towards the possibility of identifying clusters of iconic meanings. |
| Approach: | a new verb classification system is proposed to visually represent 20 classes of abstract verbs. |
| Outcome: | The proposed system could be used as a language learning aid or as linguistic comprehension tool for digital text. |
Context Matters: Enhancing Metaphor Recognition in Proverbs (2024.lrec-main)
Copied to clipboard
| Challenge: | Figurative language interpretation requires models to navigate beyond literal meaning and delve into underlying semantics of the figurative expressions. |
| Approach: | They propose to use GPT-3.5 to perform word-level metaphor detection in a zero-shot setting to examine its performance. |
| Outcome: | The proposed model performs well in identifying word-level metaphors in English proverbs in zero-shot setting. |
Multimodal and Multilingual Laughter Detection in Stand-Up Comedy Videos (2024.lrec-main)
Copied to clipboard
| Challenge: | Using TED talks, we use laughter detection software to capture humor in the sitcom genre. |
| Approach: | They develop a multimodal multilingual dataset in Russian and English with a particular emphasis on laughter detection techniques. |
| Outcome: | The proposed model outperforms peak detection and machine learning, while the latter shows promise and warrants further study. |