Papers by Dimosthenis Antypas
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research (2023.findings-emnlp)
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Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Leonardo Neves, Kiamehr Rezaee, Luis Espinosa-Anke, Jiaxin Pei, Jose Camacho-Collados
| Challenge: | specialised language models (LMs) have shown to exhibit lower perplexity and higher downstream performance across the board. |
| Approach: | They propose a benchmark for NLP evaluation in social media, SuperTweetEval. |
| Outcome: | The proposed benchmark shows that social media models perform better when compared to general-purpose models, metrics and benchmarks. |
Twitter Topic Classification (2022.coling-1)
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Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados, Vitor Silva, Leonardo Neves, Francesco Barbieri
| Challenge: | Existing methods to identify topics from posts are difficult to interpret and can differ from corpus to corpus. |
| Approach: | They propose a task based on tweet topic classification and release two datasets that can be used to train and test models. |
| Outcome: | The proposed task is based on two datasets from recent time periods and provides training and testing data. |
COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter (2021.acl-srw)
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| Challenge: | Social media is used by individuals and organisations as a platform to spread misinformation. |
| Approach: | They compile a large corpus of tweets related to coronavirus and perform an analysis to discover patterns with respect to vocabulary usage. |
| Outcome: | The proposed model based on lexical features is effective in identifying misinformation-related tweets with accuracy over 80%. |
Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation (2025.coling-main)
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Joanne Boisson, Zara Siddique, Hsuvas Borkakoty, Dimosthenis Antypas, Luis Espinosa Anke, Jose Camacho-Collados
| Challenge: | Recent advances in large language models (LLMs) have shown to be difficult to extract metaphors from free text because they can involve some implicit concepts and link dissimilar concepts. |
| Approach: | They compare the ability of large language models to extract metaphors from literary texts using domain experts. |
| Outcome: | The proposed models can extract metaphors from literary texts without using domain experts. |
TweetNLP: Cutting-Edge Natural Language Processing for Social Media (2022.emnlp-demos)
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Jose Camacho-collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa Anke, Fangyu Liu, Eugenio Martínez Cámara
| Challenge: | TweetNLP is an integrated platform for natural language processing in social media. |
| Approach: | They propose a Python-based platform for natural language processing in social media that supports a variety of NLP tasks. |
| Outcome: | The proposed platform supports generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. |
Multilingual Topic Classification in X: Dataset and Analysis (2024.emnlp-main)
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| Challenge: | Social media platforms such as X (Twitter), Snapchat and Instagram provide an environment for content creation and information sharing. |
| Approach: | They propose a multilingual dataset featuring tweet topic classification in four languages . they leverage X-Topic to perform cross-linguistic and multilingual analysis . |
| Outcome: | The proposed dataset includes topics in four languages and is useful for cross-linguistic analysis and the development of robust multilingual models. |