Dimitris Gkoumas


2024

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Common European Language Data Space
Georg Rehm | Stelios Piperidis | Khalid Choukri | Andrejs Vasiļjevs | Katrin Marheinecke | Victoria Arranz | Aivars Bērziņš | Miltos Deligiannis | Dimitris Galanis | Maria Giagkou | Katerina Gkirtzou | Dimitris Gkoumas | Annika Grützner-Zahn | Athanasia Kolovou | Penny Labropoulou | Andis Lagzdiņš | Elena Leitner | Valérie Mapelli | Hélène Mazo | Simon Ostermann | Stefania Racioppa | Mickaël Rigault | Leon Voukoutis
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

The Common European Language Data Space (LDS) is an integral part of the EU data strategy, which aims at developing a single market for data. Its decentralised technical infrastructure and governance scheme are currently being developed by the LDS project, which also has dedicated tasks for proof-of-concept prototypes, handling legal aspects, raising awareness and promoting the LDS through events and social media channels. The LDS is part of a broader vision for establishing all necessary components to develop European large language models.

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Investigating Political Ideologies through the Greek ParlaMint corpus
Maria Gavriilidou | Dimitris Gkoumas | Stelios Piperidis | Prokopis Prokopidis
Proceedings of the IV Workshop on Creating, Analysing, and Increasing Accessibility of Parliamentary Corpora (ParlaCLARIN) @ LREC-COLING 2024

This paper has two objectives: to present (a) the creation of ParlaMint-GR, the Greek part of the ParlaMint corpora of debates in the parliaments of Europe, and (b) preliminary results on its comparison with a corpus of Greek party manifestos, aiming at the investigation of the ideologies of the Greek political parties and members of the Parliament. Additionally, a gender related comparison is explored. The creation of the ParlaMint-GR corpus is discussed, together with the solutions adopted for various challenges faced. The corpus of party manifestos, available through CLARIN:EL, serves for a comparative study with the corpus of speeches delivered by the members of the Greek Parliament, with the aim to identify the ideological positions of parties and politicians.

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Overview of the CLPsych 2024 Shared Task: Leveraging Large Language Models to Identify Evidence of Suicidality Risk in Online Posts
Jenny Chim | Adam Tsakalidis | Dimitris Gkoumas | Dana Atzil-Slonim | Yaakov Ophir | Ayah Zirikly | Philip Resnik | Maria Liakata
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)

We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level (‘Low’, ‘Mod- erate’ or ‘High’) and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings.

2023

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Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia.
Dimitris Gkoumas | Matthew Purver | Maria Liakata
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Dementia is associated with language disorders which impede communication. Here, we automatically learn linguistic disorder patterns by making use of a moderately-sized pre-trained language model and forcing it to focus on reformulated natural language processing (NLP) tasks and associated linguistic patterns. Our experiments show that NLP tasks that encapsulate contextual information and enhance the gradient signal with linguistic patterns benefit performance. We then use the probability estimates from the best model to construct digital linguistic markers measuring the overall quality in communication and the intensity of a variety of language disorders. We investigate how the digital markers characterize dementia speech from a longitudinal perspective. We find that our proposed communication marker is able to robustly and reliably characterize the language of people with dementia, outperforming existing linguistic approaches; and shows external validity via significant correlation with clinical markers of behaviour. Finally, our proposed linguistic disorder markers provide useful insights into gradual language impairment associated with disease progression.

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A Digital Language Coherence Marker for Monitoring Dementia
Dimitris Gkoumas | Adam Tsakalidis | Maria Liakata
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

The use of spontaneous language to derive appropriate digital markers has become an emergent, promising and non-intrusive method to diagnose and monitor dementia. Here we propose methods to capture language coherence as a cost-effective, human-interpretable digital marker for monitoring cognitive changes in people with dementia. We introduce a novel task to learn the temporal logical consistency of utterances in short transcribed narratives and investigate a range of neural approaches. We compare such language coherence patterns between people with dementia and healthy controls and conduct a longitudinal evaluation against three clinical bio-markers to investigate the reliability of our proposed digital coherence marker. The coherence marker shows a significant difference between people with mild cognitive impairment, those with Alzheimer’s Disease and healthy controls. Moreover our analysis shows high association between the coherence marker and the clinical bio-markers as well as generalisability potential to other related conditions.

2021

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European Language Grid: A Joint Platform for the European Language Technology Community
Georg Rehm | Stelios Piperidis | Kalina Bontcheva | Jan Hajic | Victoria Arranz | Andrejs Vasiļjevs | Gerhard Backfried | Jose Manuel Gomez-Perez | Ulrich Germann | Rémi Calizzano | Nils Feldhus | Stefanie Hegele | Florian Kintzel | Katrin Marheinecke | Julian Moreno-Schneider | Dimitris Galanis | Penny Labropoulou | Miltos Deligiannis | Katerina Gkirtzou | Athanasia Kolovou | Dimitris Gkoumas | Leon Voukoutis | Ian Roberts | Jana Hamrlova | Dusan Varis | Lukas Kacena | Khalid Choukri | Valérie Mapelli | Mickaël Rigault | Julija Melnika | Miro Janosik | Katja Prinz | Andres Garcia-Silva | Cristian Berrio | Ondrej Klejch | Steve Renals
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Europe is a multilingual society, in which dozens of languages are spoken. The only option to enable and to benefit from multilingualism is through Language Technologies (LT), i.e., Natural Language Processing and Speech Technologies. We describe the European Language Grid (ELG), which is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to upload, share and distribute their services, products and resources. At the end of our EU project, which will establish a legal entity in 2022, the ELG will provide access to approx. 1300 services for all European languages as well as thousands of data sets.

2020

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Verbal Aggression as an Indicator of Xenophobic Attitudes in Greek Twitter during and after the Financial Crisis
Maria Pontiki | Maria Gavriilidou | Dimitris Gkoumas | Stelios Piperidis
Proceedings of the Workshop about Language Resources for the SSH Cloud

We present a replication of a data-driven and linguistically inspired Verbal Aggression analysis framework that was designed to examine Twitter verbal attacks against predefined target groups of interest as an indicator of xenophobic attitudes during the financial crisis in Greece, in particular during the period 2013-2016. The research goal in this paper is to re-examine Verbal Aggression as an indicator of xenophobic attitudes in Greek Twitter three years later, in order to trace possible changes regarding the main targets, the types and the content of the verbal attacks against the same targets in the post crisis era, given also the ongoing refugee crisis and the political landscape in Greece as it was shaped after the elections in 2019. The results indicate an interesting rearrangement of the main targets of the verbal attacks, while the content and the types of the attacks provide valuable insights about the way these targets are being framed as compared to the respective dominant perceptions and stereotypes about them during the period 2013-2016.