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.

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.

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.

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.

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.